Stats Chat

From Stuff, on the housing affordability index

“The university said a key driver was the median house price, which rose more than $30,000 over the year, eclipsing the $19.35 increase in average weekly wages.

Interest rates also rose from 5.51 per cent to 5.97 per cent on average.”

Comparing the median house price increase to the median (I think) individual weekly wage and salary income increase is a particularly opaque way of presenting the data. Obviously $30,000 is a lot more than $19.35, but one is paid over thirty years an the other is received over one week.

For example, it should be easy to say what increase in average weekly earnings would be necessary to not be ‘eclipsed’ by the $30,000 house price increase? If the report doesn’t say, the journalist should ask. The reader shouldn’t have to do that calculation. It turns out that if median weekly wages had risen $34.50 instead of $19.35, they wouldn’t have been eclipsed and the affordability index would have stayed constant. This isn’t the impression that you’d get from the story.

The argument for an affordability index is that it makes affordability changes easier to understand by reducing them to a single number.  That’s only true either if you understand how the number is calculated (which takes quite a lot of research) or you don’t really care exactly what it means.

An interesting interactive calculator for heart disease/stroke risk, from the University of Nottingham. It lets you put in basic, unchangeable factors (age,race,sex), modifiable factors (smoking, diabetes, blood pressure, cholesterol), and then one of a set of interventions

Here’s the risk for an imaginary unhealthy 50-year old taking blood pressure medications

The faces at the right indicate 10-year risk: without the unhealthy risk factors, if you had 100 people like this, one would have a heart attack, stroke, or heart disease death over ten years, with the risk factors and treatment four  would have an event (the pink and red faces).  The treatment would prevent five events in 100 people, represented by the five green faces.

There’s a long list of possible treatments in the middle of the page, with the distinctive feature that most of them don’t appear to reduce risk, from the best evidence available. For example, you might ask what this guy’s risk would be if he took vitamin and fish oil supplements. Based on the best available evidence, it would look like this:

The main limitation of the app is that it can’t handle more than one treatment at a time: you can’t look at blood pressure meds and vitamins, just at one or the other.

(via @vincristine)

• Studies showing that bilingualism is good for the brain are more likely to get published, (on NPR)

• “When 2000 people take aspirin for one year, one heart attack is prevented.” A story on absolute risk and number-needed-to-treat, at the NY Times Upshot blog.  They introduce this as related to personalised medicine, but it’s really not.

• “Five of the 21 models in the [Short Range Ensemble Forecast] had less than 10 inches of snow falling. Nine of the 21 predicted a foot or less.”  538 on the Not-So-Great New York Blizzard of 2015. Also, the National Weather Service now has probabilistic snowfall maps based on this set of models.
  
• A couple of new interactive maps from Harkanwal Singh at the Herald: changes in median rent and in commuting patterns

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.”

From the Herald

A study from the Garvan Institute in Australia demonstrates that a diet rich in coconut oil protects against ‘insulin resistance’ (an impaired ability of cells to respond to insulin) in muscle and fat and avoids the accumulation of body fat caused by other high fat diets.

Suppose we wanted to find this study and see what it really demonstrates. There’s not a  lot to go on, but the Google knows all and sees all. When you have more information — researcher names, journal names, words from the title of the paper — Google Scholar is the best bet (as Jeff Leek explains here).  With just “Garvan Institute coconut oil”, Google Scholar isn’t very helpful.

However, since this study is popular among coconut lobbyists, an ordinary Google search does quite well. For me, the top hit is a press release from the Garvan Institute. The press release begins

A new study in animals demonstrates that a diet rich in coconut oil protects against ‘insulin resistance’ (an impaired ability of cells to respond to insulin) in muscle and fat. The diet also avoids the accumulation of body fat caused by other high fat diets of similar calorie content. Together these findings are important because obesity and insulin resistance are major factors leading to the development of Type 2 diabetes.

I’ve highlighted two key phrases: this was an animal study, and the coconut oil diet did well compared to another high fat, high calorie diet.

What’s more, the Garvan press release links to the research paper. The abstract is open-access; here are two quotes from it

Mice fed the MCFA diet displayed reduced adiposity and better glucose tolerance than LCFA-fed animals.

In rats, isocaloric feeding of MCFA or LCFA HF diets induced hepatic insulin resistance to a similar degree, however insulin action was preserved at the level of LF controls in muscle and adipose from MCFA-fed animals.

That is, in mice, coconut oil was better than the same amount of lard (though not as good as a low-fat diet); in rats coconut oil was as bad as lard on one measure of insulin resistance, but was comparable to the low-fat diet on another measure.

If the results translated to humans, this would show a diet high in coconut oil was better for insulin resistance than one high in animal fat, but worse than a low-fat diet.

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.”

I expected the Herald story “Hay fever pills linked to Alzheimer’s risk – study” to be the usual thing, where a fishing expedition found a marginal correlation in low-quality data.  It isn’t.

The first thing I noticed  when I found the original article is that I know several of the researchers. On the one hand that’s a potential for bias, on the other hand, I know they are both sensible and statistically knowledgeable. The study has good quality data: the participants are all in one of the Washington HMOs, and there is complete information on what gets prescribed for them and whether they fill the prescriptions.

One of the problems with drug:disease associations is confounding by indication. As Samuel Goldwyn observed, “Any man who goes to a psychiatrist needs to have his head examined”, and more generally the fact that medicine is given to sick people tends to make it look bad.  In this case, however, the common factor between the medications being studied is an undesirable side-effect for most of them, unrelated to the reason they are prescribed.  In addition to reducing depression or preventing allergic reactions, these drugs also block part of the effect of the neurotransmitter acetylcholine. The association remained just as strong when recent drug use was excluded, or when antidepressant drugs were excluded, so it probably isn’t that early symptoms of Alzheimer’s lead to treatment.

The association replicates results found previously, and is quite strong, about four times the standard error (“4σ”) or twice the ‘margin of error’. It’s not ridiculously large, but is enough to be potentially important: a relative rate of about 1.5.

It’s still entirely possible that the association is due to some other factor, but the possibility of a real effect isn’t completely negligible. Fortunately, many of the medications involved are largely obsolete: modern hayfever drugs (such as fexofenadine, ‘Telfast’) don’t have anticholinergic activities, and nor do the SSRI antidepressants. The exceptions are tricyclic antidepressants used for chronic pain (where it’s probably worth the risk) and the antihistamines used as non-prescription sleep aids.

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.”

There’s an interesting piece in the Herald arguing that the effective school fees you pay by living in one of the top school zones in Auckland aren’t great value, and that you’d be better off just paying private-school fees explicitly. It’s a good point, but I think the calculations in the article are missing something:

Where a school zone boundary sliced through the middle of a suburban street, in-zone houses were up to $272,000 more expensive than comparable properties on the other side of the road.

“Over the life of a 20-year mortgage, at a fixed mortgage rate of 6.5 per cent, the extra $272,000 it costs to buy a home ‘in-zone’, with interest, equates to an outlay of $486,710. That’s almost half a million dollars.“

That’s compared to private-school fees that could easily total “more than $100,000 per student over five years”.

There are two points that don’t get addressed explicitly in the article. Firstly, many people have more than one child. Secondly, the money spent on school-zone real estate isn’t gone, it’s a speculative investment.

Using their figures (because I’m lazy), if you subtract two kids at $100,000 school fees from the $486,710 real-estate plus interest you get $286,710. If the real-estate premium for the school zones keeps up with inflation, you basically break even, with the possibility of a big loss (if boundaries are redrawn) or a big gain (if prices keep going up).

If you’re the sort of person the article is aimed at, there’s a good chance you’ve already got more of your money in Auckland real estate than is ideal, so speculating on the Grammar Zone premium might not be a good investment, but it’s not self-evidently bad.

There are a couple of surprising points about the article. First, you would hope this is the sort of calculation anyone would already be doing before planning to spend the thick end of million bucks. Second, the fact that real-estate prices can go up as well as down is not something the Herald usually misses.

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.”