Posts written by Atakohu Middleton (125)

Every year, the Department of Statistics offers summer scholarships to a number of students so they can work with staff on real-world projects. Oliver, right, is working on a project called Maps and graphics for animal populations with Associate Professor Rachel Fewster. Oliver explains:

“This project involves dealing with data from various conservation projects around the country. The data primarily consists of catch rates of various animal species at different locations of a project. My job is to come up with new ideas for maps, graphics and charts that conservation volunteers will find engaging, and that will illustrate the positive impact their work is having on New Zealand’s environment.

“The project is aimed at motivating the general public who are involved in local conservation schemes. When they return from a day’s work, they will get to see the rewards of their labours presented on a map, as well as personalised charts showing their own contribution to the project. Ideally, this keeps them motivated and coming back for more!

“I recently completed my Bachelor of Science majoring in Statistics and minoring in Psychology at the University of Otago. I am originally from Auckland, and have returned to pursue a Bachelor of Science (Honours) in Statistics in 2015.

“I enjoy statistics as I believe it can be applied to almost any aspect of life. Data exists in so many subjects and occupations: commerce, medicine, law, sports, the environment – anything you can think of!

“Where there is data, we can use statistics to gain a deeper understanding of the underlying processes taking place and better understand the world around us. Because statistics covers such a wide range of topics, I’m always working with something different, which keeps the subject interesting.

“This summer, hopefully I will find some time to get away and do some camping and get the chance to play a few games of cricket in the sun.”

Every year, the Department of Statistics offers summer scholarships to a number of students so they can work with staff on real-world projects. Kai, right, is working on a project called Constrained Additive Ordination with Dr Thomas Yee. Kai explains:

“In the early 2000s, Dr Thomas Yee proposed a new technique in the field of ecology called Constrained Additive Ordination (CAO) that solves the problems about the shape of species’ response curves and how they are distributed along unknown underlying gradients, and meanwhile the CAO-oriented Vector Generalised Linear and Additive Models (VGAM) package for R has been developed. This summer, I am compiling code for improving performance for the VGAM package by facilitating the integration of R and C++ under the R environment.

“This project brings me the chance to work with a package in worldwide use and stimulates me to learn more about writing R extensions and C++ compilation. I don’t have any background in ecology, but I acquired a lot before I started this project.

“I just have done the one-year Graduate Diploma in Science in Statistics at the University of Auckland after graduating from Massey University at Palmerston North with a Bachelor of Business Studies in Finance and Economics. In 2015, I’ll be doing an honours degree in Statistics. Statistics is used in every field, which is awesome to me.

“This summer, I’ll be spending my days rationally, working with numbers and codes, and at night, romantically, spending my spare time with stars. Seeing the movie Interstellar [a 2014 science-fiction epic that features a crew of astronauts who travel through a wormhole in search of a new home for humanity] reignited my curiosity about the universe, and I have been reading astronomy and physics books in my spare time this summer. I even bought an annual pass to Stardome, the planetarium at Auckland, and have spent several evenings there.”

Every year, the Department of Statistics offers summer scholarships to a number of students so they can work with staff on real-world projects. Eric, right, is working on a project called Accessible graphics for data on maps with Professor Chris Wild. Eric explains:

“I am working on an easy-to-use data-analysis system called iNZight  that has been developed by Professor Chris Wild and his students at the University of Auckland. The primary purpose of iNZight is to allow students to experience exploring many different types of statistics, and it has been successfully deployed in many situations to produce significant results.

“My main task is to implement a simple geographical information system (GIS) in iNZight so that students can draw maps, visualise geographical information, learn and interpret patterns they reveal.

“Knowing where things happen is important, especially in looking for or displaying spatial relationships in areas such as crime, health, education, population, environmental resource management, market analysis, highway maintenance, accident monitoring, and emergency planning and routing.

“Geographical data are also very interesting and fun to look at, and I would like to present iNZight users with visually appealing and informative maps. A picture is worth a thousand words!

“I am from South Korea. I studied applied mathematics and statistics for my undergraduate degree, and recently finished my honours degree in statistics at the University of Auckland. I am hoping to study a masters in 2015.

“I am fascinated by patterns hidden inside data that can only be seen by using appropriate statistical methods. Learning different statistical techniques to effectively bring out the patterns is naturally my biggest interest and passion.

“I particularly love statistics because of its wide range of use in many areas such as finance, ecology, computing and many more.”

Every year, the Department of Statistics at the University of Auckland offers summer scholarships to a number of students so they can work with staff on real-world projects. Rahul Singhal, right, is working on a project called Developing Bias Weights for the New Zealand Longitudinal Census with Professor Alan Lee. Rahul explains:

“The project attempts to adjust for linkage bias in the New Zealand longitudinal census – to reduce this bias as much as possible.

“When we link people from one census to another, those people who have been linked may differ from those that could not be linked, that is, the non-linked people may have different characteristics from the linked people.

“The bias can result in a tendency to overestimate or underestimate important relationships between variables, such as the effect of a person’s occupation on mortality risk.  This tendency could potentially result in incorrect conclusions. Thus, this project could be very helpful for other projects that use the New Zealand Longitudinal Census to investigate the effect of different variables.

“I have just finished my conjoint BA/BCom degree in Statistics, Economics, Accounting and Finance.  Statistics has interested me ever since I took the Statistics 108 course, Statistics for Commerce, in which I learned about the power and flexibility of statistics. It is the main reason why I decided to go from a single degree to a conjoint degree.

“I don’t have too much planned for my summer break, just visiting family in India, as I haven’t seen them for a few years.”

Every year, the Department of Statistics offers summer scholarships to a number of students so they can work with staff on real-world projects. Bo, right, is working on a project called Construction of life-course variables for the New Zealand Longitudinal Census (NZLC) with Roy Lay-Yee, Senior Research Fellow at the COMPASS Research Centre, University of Auckland, and Professor Alan Lee of Statistics. Bo explains:

“The New Zealand Longitudinal Census has linked individuals across the 1981-2006 New Zealand censuses. This enables the assessment of life-course resources with various outcomes.

“I need to create life-course variables such as socio-economic status, health, education, work, family ties and cultural identity from the censuses. Sometimes such information is not given directly in the census questions, but several pieces of information need to be combined together.

“An example is the overcrowding index that measures the personal living space. We need to combine the age, partnership status of the residents and number of bedrooms in each dwelling to derive the index.

“Also, the format of the questionnaire as well as the answers used in each census were rather different, so data-cleaning is required. I need to harmonise information collected in each census so that they are consistent and can be compared over different censuses. For example, in one census the gender might be given code ‘0’ and ‘1’ representing female and male, but in another census the gender was given code ‘1’ and ‘2’. Thus the code ‘1’ can mean quite different things in different censuses. My job is to find these differences and gaps in each census.

“The results of this project will enable future studies based on New Zealand longitudinal censuses, say, for example, the influence of life-courses variables on the risk of mortality. This project will also be a very good experience for my future career, since data-cleaning is a very important process that we were barely taught in our courses but will actually cost almost one-third of the time in most real-life projects. When we were studying statistics courses, most data sets we encountered were “toy” data sets that had fewer variables and observations and were clean. However, in real life, as in this case, we often meet with data that have millions of observations, hundreds of variables, and inconsistent variable specification and coding.

“I hold a Bachelor of Commerce in Accounting, Finance and Information Systems. I have just completed Postgraduate Diploma in Science, majoring in Statistics, and in 2015, I will be doing Master of Science in Statistics.

“When I was studying information systems, my lecturer introduced several statistical techniques to us and I was fascinated by what statistics is capable of in the decision-making process. For example, retailers can find out if a customer is pregnant purely based on her purchasing behaviour, so the retailers can send out coupons to increase their sales. It is amazing how we can use statistical techniques to find that little tiny bit of useful information in oceans of data. Statistics appeals to me as it is highly useful and applicable in almost every industry.

“This summer, I will spend some time doing road trips – hopefully I can make it to the South Island this time. I enjoy doing road trips alone every summer as I feel this is the best way to get myself refreshed and motivated for the next year.”

Every year, the Department of Statistics offers summer scholarships to a number of students so they can work with staff on real-world projects. Yiying, right, is working on a project called Modelling Competition and Dispersal in a Statistical Phylogeographic Framework with Dr Stéphane Guindon.  Yiying explains.

 “The processes that govern the spatial distribution of species are complex. Traditional approaches in ecology generally rely on the hypothesis that adaptation to the environment is the main force driving this distribution.

“The supervisors of this project propose an alternative explanation that assumes that species are found in certain places simply because they were the first to colonise these locations during the course of evolution. They have recently designed a stochastic model that explains the observed spatial distribution of species using a combination of dispersal events (i.e., species migrating to new territories) and competition between species.

“In this project, I will run in silico [computer] experiments and analyse real data in order to validate the software Phyloland that implements our dispersal-competition model.

“To validate the model, we will randomly generate ‘true value’. Then we will use the model to make estimations of the true value. If the estimated values match the true value relatively closely, then the model is reliable.

“I am doing a BCom/BSc conjoint degree. My majors are Finance, Accounting and Statistics – 2015 is my fourth year. I am planning to do an Honours degree in statistics, so this summer research project is a very valuable experience for me.

“I enjoy statistics because it brings me closer to the real world. Sometimes, things are not simply what we see. Without data, we would never have convincing evidence about what is really happening. The amount of information out there is massive and statistics can help people tell how reliable a statement is. Studying statistics has helped me make better use of information and think more critically.

“My plans for summer include relaxing and reading more books. And having plenty of sleep.”

Every year, the Department of Statistics offers summer scholarships to a number of students so they can work with staff on real-world projects. Alexander, right, is undertaking a statistics education research project with Dr Marie Fitch and Dr Stephanie Budgett. Alexander explains:

“Essentially, what this project involves is looking at how bootstrapping and re-randomisation being added into the university’s introductory statistics course have affected students’ understanding of statistical inference, such as interpreting P-values and confidence intervals, and knowing what can and can’t be justifiably claimed based on those statistical results.

“This mainly consists of classifying test and exam questions into several key categories from before and after bootstrapping and re-randomisation were added to the course, and looking at the change (if any) in the number of students who correctly answer these questions over time, and even if any common misconceptions become more or less prominent in students’ answers as well.

“This sort of project is useful as traditionally, introductory statistics education has had a large focus on the normal distribution and using it to develop ideas and understanding of statistical inference from it. This results in a theoretical and mathematical approach, which means students will often be restricted by the complexity of it and will therefore struggle to be able to use it to make clear inference about the data.

“Bootstrapping and re-randomisation are two techniques that can be used in statistical analysis and were added into the introductory statistics course at the university in 2012. They have been around for some time, but have only become prominent and practically useful recently as they require many repetitions of simulations, which obviously is better-suited to a computer rather than a person. Research on this emphasises how using these techniques allow key statistical ideas to be taught and understood without a lot of fuss, such as complicated assumptions and dealing with probability distributions.

“In 2015, I’ll be completing my third year of a BSc in Statistics and Operations Research, and I’ll be looking at doing postgraduate study after that. I’m not sure why statistics appeals to me, I just found it very interesting and enjoyable at university and wanted to do more of it. I always liked maths at school, so it probably stemmed from that.

“I don’t have any plans to go away anywhere so this summer I’ll just relax, enjoy some time off in the sun and spend time around home. I might also focus on some drumming practice, as well as playing with my two dogs.”

From our friends at Simply Stats: A non-comprehensive list of awesome things other people did in 2014. See number 10 of the 36 in particular (aw, thanks, guys) and any one of the many on the list that talks about R and its loin fruit. R was born in the mid-90s at the Department of Statistics at the University of Auckland and has since turned into a monster. I never ever get tired of telling people that. And speaking of R, a feature praising the little letter that did has been published in Nature.

This from the latest Thomas Lumley Listener column: “Statistics uses both real and theoretical randomness for a lot of things, from selecting phone numbers in polling and allocating treatments in clinical trials, to proving that a set of mathematical assumptions does or doesn’t let you distinguish correlation from causation.

“So what do we think “random” really means?”

Read the column here.

“…Unlike people, computer programs aren’t embarrassed by their prejudices and don’t try to hide them.” Thomas Lumley discusses the algorithm-based tools that sift data about us and our behaviour in this week’s New Zealand Listener. Click here for the full story.