Post 3: A News Analysis

In the definition of human, we can say humans are self-aware social mammals generally possessing the ability to reason, speak, and use complex tools. Human started to use tools since 2.5 million ago, and the biggest difference between human and animals is that human could use tools but animals could not.

Nowadays, we are live in the world of big data. Artificial intelligence is one of the famous representative for the tool of big data age. In general, however, statistics field are still using traditional method to calculate and applied in real world. In this situation, the “StatScale” project may bring us some new changes in tool of statistics.

I read a new from Science Daily named “Next generation of statistical tools to be developed for the big data age” last week. In this news, it shows we will have more than 30 billion devices collecting data streams by 2020. At this time, traditional statistic is only useful in limited data, it is not works very well when we have too much data like today’s world. But the “StatScale” project could solve this problem for us. This is a program funded by the Engineering and Physical Sciences Research Council and developed from the members of Lancaster University's Data Science Institute in partnership with colleagues in the University of Cambridge's Statistical Laboratory. This project already applied in several industry organizations. For example, Shell UK, BT, AstraZeneca and the office for National Statistics. After this project finished, we could easily predict that statistic will have more benefits for modern economic and society.

Big data age brings a convenient life to us. However, some questions we still need to ask ourselves. Do I reduce the time of thinking? I believe this is the biggest problem in big data age. I have to approval that big data helps us a lot. Predicting weather forecast, analysis of intelligent behavior and management of economic trend are all need big data and statistics. But let’s thinking, is there any bad effect from big data? The answer is yes. Since the big data became popular, more and more people prefer to peeping the privacy of others. Hillary Clinton email controversy is a famous case in recently years. I don’t want to judge this case, but it is basic on the big data and information disclosure problem.

All in all, statistics in big data is the trends of world. I believe it have more advantages than disadvantages, but people need to know how to “use” and “manage” big data. At meanwhile, “StatScale” program will also bring some more good news in statistics field, and the future of statistics field will be better.

Here is the link of news, thanks for reading!

https://www.sciencedaily.com/releases/2016/09/160921115229.htm

Post 2: Introduce a Professional Journal in Statistics

I believe that most students are familiar with professional journal and magazine. Reading journal or magazine could increase professional knowledge and learn more news about particular field. For myself, I read a journal named “Variational Inference of Linear Regression with Nonzero Prior Means” in NDSU library database recently.

This is a professional journal in statistics field which is my major. It consists with abstract, introduction, the linear regression model, variational inference of the linear model, simulations and conclusions. The important thing worth to mention that this journal is not really difficult to reading, I think some beginners of statistic also could understand this journal. Now I am going to introduce this interesting journal from the advantages and disadvantages.

Bayesian method and linear regression are the key word for this journal, the model used in article is based on regression theorem. So even I said this journal is not very difficult, the readers still need to know what is the basic meaning of Bayes and linear regression before start reading.

This journal has many strong points. First of all, the vocabularies could easily be understood for students especially undergraduate students. In the previous time, vocabularies are not familiar with students in some of journals I read is common. That means you have to read and check a lot of additional material to support your reading. Reading other supporting books while reading one journal makes me feel incoherent and I don’t like that. I think you also don’t like that, do you? Secondly, graphs are important in science professional journal and this journal did very well in this aspect. In the simulation part, the authors used graphs to compare the efficiency of VLR-NZE and VLR, the PD and FDR performance of VLR-NZE and VLR and the performance of VLR-NZE and VLR. Part of prior mean of the nonzero coefficients in models are known. You may feel confused when I talk about these compare performance, but you could easily understand the meaning of this model and conclusion with graphs in this journal. Lastly, the logic in this journal are strict and the structure is rigorous. The writers explain the problem first, then they explain the solution and conclusion step by step. I think this is significant important in any science professional journal.

I recommend this journal to everyone who interesting in statistics field, however, it still have some disadvantages. I think the supporting quotations is too much. I know and understand professional journal need use quotations, but the number of quotation in this journal is too much. This makes me tired and whiny when I read it.

All in all, I have to say I really like this journal. If you want to read some professional journal in statistics, I recommend this one to you. Here is the like of this journal.  I am sure you will like it! Thanks for reading, enjoy the professional journal.

Journal Link:

http://www-tandfonline-com.ezproxy.lib.ndsu.nodak.edu/doi/full/10.1080/03610918.2014.895835

Post 1 An explanation of Statistics

“What’s your major?”
“Statistics.”
“Oh my god, why do you choose statistics? Do you need to take many mathematics courses?”
“Yes, and I like it.”
“That’s unbelievable.”

This an interesting dialogue happened on me in my chemistry lab this week. At first I confused for his surprise, after a while later, I thought that may because he “hates” numbers like mathematics as many people.

But for now, I have some questions. Do you like mathematics? How do you think about statistics? Do you comprehend the meaning of statistics? Since I am majoring in statistics, today I will introduce this major and this field to you.

Let’s talk about the definition of statistics first. Collection, analysis, interpretation, presentation and organization of data are the main jobs/processes need to be done in statistics. For example, weather forecast is a typical application of statistics nowadays. At the beginning, people need to collect and record the weather situations from the previous years. Then using software like SAS or R to analyze the data. After finish all the preparatory work, the analysis results could be used to serve in our life. That is the rough formation of the weather forecast. Meanwhile, we have many additional examples like weather forecast in statistics in our daily life.

What is the basis of statistics? Like any other engineer subject, mathematical background could deepen understanding in statistical techniques. Most time people who working in the field of statistics have to specialize to calculus because calculus need to be used for calculating the probability. Besides, linear algebra is another basic tool in statistics. This information could be found in job posting or the requirement of graduate school. What’s more, I always think programming skill is also necessary for statistician or data analyst since computer program could finish analysis data faster and accuracy.

I know many people who were graduate from statistics are working in insurance company, bank and survey research company. Working in these company are organizing and clear, which means people just need to complete their own work at most time. However, achieving overall co-ordination is never a bad thing.

The world we are living is component of big data. I heard many people said that we never use statistics in our daily life even we learned a lot about it. But to be honest, I think this idea is total wrong. As I have said, weather forecast is a good example. If you think this example is far from your life. All right, we need to eat every day, we need to sleep every day, and we need to breath every second. But data analysts have analyzed how the agriculture growth, how many hours do we need to sleep for the best state, and even the air pollution index. From these examples, nobody could say statistics is useless.

Here is the basic information about statistics field. I like this major, hope you all have learned more about this area and could like it too.