Partitioning the Variation in Data

Introduction 

One of the basic questions we can ask for in data analysis is: "What is the difference?" Although I think this is basic, I realized that I was not really designing most of the things I thought. The problem that is not asked in this question is that it can often lead to many unprofessional and long-term jobs. Take time to ask yourself: "Why can I find out that he or she can explain why this behaviour or change is different?" It often can make you understand that you actually know more than you think you are doing. Developing an understanding of the source of data is an important objective of analyzing the data.

When analyzing data analysis, essentially before you look at the information, it is useful to divide the data gap. This can be allocated to diverse categories: a fixed and a loss. In each of these categories, there may be a series of different types of screening.

Fixed Variation

Regular data changes are attributed to the unique features of the world. If we are to review the data, the changes in the data that describe the fixed attributes will be the same. The typical pattern of behaviour is the sequence of data series. If you need to look at a series of series of deaths in the US for a few years, you will see that death is likely to be higher than in the winter and under the summer. Careful and precise about the ozone-free weather, you will see that the ozone is higher than in the summer and is low in the winter. In each of these examples, the seasons are regularly annually. Previously, the explanation should be made with the atmospheric chemical nature; Death death, the explanation is unclear and very difficult (which may be due to totalitarianism).

Data with a varied set does not necessarily mean that there is always the same value each time the data is, but the overall data structure is the same. If the data is different for each test, it may be due to a sudden change, and we will discuss it in the next section.

It is important to understand the different ways to differentiate your data so that you can often collect information about specific features that use it directly in any way you can. For example, the present time is the easiest way to add to it because we have discovered the times when it starts and ends. Use of the equivalent service for one month or a quarter is usually functioning.

An explanation of your changes in your data by presenting specific features in the form can reduce the lack of quality and improve the quality or accuracy. However, this may require a lot of work, exiting and collecting more information or recovering a lot of variables. However, this process ultimately deserves it. Attempting to change the nature of natural data discarded is a waste of time and is likely to rise to the level of freedom of the system.

When our experience analyzes biomedical data, I discovered that the diversity of data can be described as specific characteristics: age, gender, location, time, temperature, etc. In fact, it is often described as unnecessary and unnecessary in many different ways. However, a complex issue in this method is that it requires a deeper understanding of the surrounding data structure, as well as a good relationship with a garden expert who can help you to inform the diverse sources. Investing in more information about the data, before it happens in the data, can save you a lot of time in the process of data analysis.

Random variation

When we divide the diversity of data that can be set to the definitive attributes, it is still different variants. Sometimes we try to look at the data that says that all the changes are uneven because we can then show that we do not collect information about any other form. The development of new and modern models can be interesting and exciting, but we face it, we can generally eliminate all the need for it by collecting good information. It's useful, at least, to evaluate what's going on with a variety of changes and collect additional data needed.

An unusual change makes the data change every time we make it. Although we can make sure that the ozone will be high in the summer (compared to the winter), it does not mean that it will always be 90 parts per billion pounds on June 30. It can be 85 ppb per year and 96 ppb in the other year. These conflicts can not simply describe a specific event, so it may be possible to identify different conflicts. The key to remembering the diversity of data Dissociation is unequivocal in the unmatched variable characteristics.

Sometimes it is called indiscriminate change that is easy to do "the rest of the time" that we can not adapt to certain features. However, this is an automatic way to see the information because if there is still the feature to release a different disk file, then you may be able to lower the data analysis to a hidden or confused domain. There are several ways to verify this stage of data analysis A good example, but do it well with what you have to do before working with the exploration.

The random variation application usually looks like the financial information of the market and good reasons. The idea of a robust market suggests that, as a matter of fact, if there is a set difference (predictable) that the value of the financial assets, the market participants would benefit from the use of the information quickly arbitrage opportunities. If we did know, for example, Apple stock prices were always low in winter and high summer, you can buy in the winter and sell summer and make you free of charge. But if everyone does, ultimately the chance of mediation will disappear (as it has had on a regular basis). Any change from the stock market prices is quite different, so it is difficult to "beat the market".

Is it really random?

When I see student analyzes, and use the standard model to have a result (usually described as Y), a predictor (X) and random or erroneous (e), my first question was and always about the part of the mistakes. Usually, there is a little confused about why I would ask this, as this section is "abnormal" that you are not interested in. But when I try to bring them a discussion about why there is a random variation in data, I often find out some more variables that we would like, but we have no data.

Often, there is a great explanation of why we keep that data. My motto is not to criticize the student for lack of information, but to reinforce that these qualities are unmatched and inaccessible. The fact that you can not get information about anything does not mean you can tell a restriction decision. If these data are not collected, they may be evaluated for a reasonable alternative. Access to transfers is not the right one, but it can usually give you an idea of whether your example is completely exhaustive or not.

For example, the use of substitute involves an estimated population cigarette smoking. Some US researchers have information on tobacco-related behaviour, but there is no complete data available throughout the country. A recent study by Zeger et al., Death and air pollution, use lung cancer as a substitute. The idea here is that lung cancer is often caused by smoking, so, although it is not a good indicator of smoking, is a good alternative to the culture.

Summary

Distribution of your information in a variety of different formats may be useful exercise before looking at the data. It can lead to knowing that there are important jobs that you do not have information, but you can go and pick them up. Making the effort to collect additional data when it is proven that they can save you time and effort so that they can sort out the change as if it was a separate one. Importantly, the exclusion of the key effect of the census structure can lead to confidentiality or confusion. When the data is deleted, the unwanted variable can be converted to another variable.

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