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30.12.2016 16:58Discoveries of the year (BONUS BALTICAPP - Race Against Eutrophication Blog) Matti Sihvonen

Hi everybody,

I’m sorry that it’s been so long since my last blog post. The end of the year has been very busy. I’ve taken some courses concerning modelling, econometrics and statistics in order to learn more about the subject of my PhD. I’ve been so involved in these actions that I’ve haven’t got a time even to write this blog. I’m so sorry about that. Fortunately, the intense period is starting to be over now, at least for a while.

The courses that have kept me busy have been advanced econometrics, the extension course in introductory statistics and linking data and ecological models. All of these courses have been excellent and very useful; I’ve learned a great deal about things that are very relevant regarding my PhD. I must say that the course lecturers at the econometrics and linking data courses where exceptionally enthusiastic and inspirational. From linking data course I learned about the model comparison method called AIC, Akaike’s information criteria. I liked it so much that I used it to rank the models in the first paper. I also learned a lot about result reporting and validation methods.

From the advanced econometrics course I learned especially that I really have to examine the residuals for traces of heteroskedasticity or other distortions. I found some problems and we had to one more time to recheck the data. All kinds of transformations were tried, but as things are typically more complicated with the real data than with the book examples, we solved the problem by excluding some observations from the data set. These observations were related to some very old experiments and as such they came from the different distribution than the rest of the observations. Many things were discussed during the econometrics course and there was a lot of stuff to learn for the exam. Thus, I studied intensively for it. I’m happy to say that it went very well. Now I can finally focus entirely on finishing the first paper.

However, there is still a lot of writing to do before the first paper is finally ready. I’ve received comments from my supervisor Kari Hyytiäinen and co-author Elena Valkama. Before I could go off to my Christmas holidays I had to go through the comments and rewrite a lots of stuff as well as once again recalculate the results as the functional forms changed a little bit because of the residual distortions. Thus, there was a lot of work to do. There’s a light at the end of the tunnel; according to Elena, the paper is going to be a good one. That’s a very nice thing to keep in mind because this has been a very challenging task for me. The challenge originates from the fact that I didn’t have any background with statistics or econometrics or data analysis in general before this work, which is basically advanced biometrics, or something like that. Well, after this, I hope that the second and third papers will be easier. At least those are more related to economics, which I am more familiar with.

Currently I’m finishing the discussion section. I have this tool, which I use to organize this section. It’s kind of hard to understand what are the actual results in this kind of a paper, in which the model is built. Anyway, the paper is now almost ready and I hope that I have finished all the writing by the end of the Christmas holidays. 

Thus, the battle rages on. The life of PhD student is rather heavy, I must say. At the same time, it’s very rewarding as we learn so much new things constantly. To wrap up the year a little bit I would say that the most important thing has been the discovery of the role of statistics in research work. I’ve always thought that the mathematics is the most important language in research work but this year I’ve understand the statistics is a very important language as well. Or actually statistics should be considered as a method whereas the mathematics is clearly a language, a way to present things accurately. In addition, I’ve discovered that the computer program R is something that must be learned. It is the way of the future. It almost seems that the statistics equals R these days. Well, this is very fine as the R is free to download for everyone and as such it’s a natural choice for universal environment for statistical programming. I must keep learning R because currently I know just the very basics.

I’ve also understood that as my own mathematical capabilities are limited, the statistics are even more important to me. This is because if I’m fortunate enough to continue my resource work in the future, I should probably focus more on empirical work; working with real data. And perhaps even if my resource career would be over after this PhD program, I could work with the data in some firm, or something. In any case, I feel that the empirical work including data analysis and statistical inference might be something that I could do in the future. At least it would be more my thing than highly theoretical and mathematical work, as I’m simply not talented enough in that field. It’s important to recognize and accept ones own limitations and strengths also.

Thus, back to books, happy New Year for every one, let the year 2017 be full of new discoveries.

Best regards: Matti Sihvonen

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