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Descriptive statistics: These are descriptions that summarize information about a particular data column, such as the average value or the extreme values. Inferential statistics: Methods that allow claims to be made concerning the data with con dence. Correlation statistics: Statistics that quantify relationships within the data. Searching: Asking speci c questions concerning the data can be useful if you understand the conclusion you are trying to reach or if you wish to quantify any conclusion with more information. Grouping: Methods for organizing a data set into smaller groups that potentially answer questions. Mathematical models: A mathematical equation or process that can make predictions. The three tasks outlined at the start of this section (summarizing the data, nding hidden relationships, and making predictions) are shown in Figure 1.2 with a circle for each task. The different methods for accomplishing these tasks are also positioned on the Venn diagram. The diagram illustrates the overlap between the various tasks and the methods that can be used to accomplish them. The position of the methods is related to how they are often used to address the various tasks. Graphs, summary tables, descriptive statistics, and inferential statistics are the main methods used to summarize data. They offer multiple ways of describing the data and help us to understand the relative importance of different portions of the data. These methods are also useful for characterizing the data prior to developing predictive models or nding hidden relationships. Grouping observations can be useful in teasing out hidden trends or anomalies in the data. It is also useful for characterizing the data prior to building predictive models. Statistics are used

2.4 2 x = 0.012; vR 4:9. 2.5 Screened factors are X1;X2;X3;X4;X5 and X6. The regression has this form:

Also Known As: Big Data, Bad Data, Too Much Data Most Frequent Scale: Application, system Refactorings: Plan for Scaling, Choose the Right Data Architecture Refactored Solution Type: Process, role, architecture Root Causes: Designing by prototype, growing a solution instead of

Measurement error refers to the possibility that any individual survey observation might differ from the value that would be observed by a perfect measurement of the concept that the survey item was intended to measure. Such errors can arise for many reasons, associated with the response task, the respondent, the interviewer, the data collection mode and the interview setting (Biemer and Lyberg, 2003, chapter 4), but some features of measurement error are distinctive in the case of longitudinal surveys.

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24: System Architecture Issues styles Instead, we'll present you with a 30 second introduction Although many themes and variations exist, if you ignore the code hacking with no design proponents you are left with three main forms: waterfall, iterative, and the latest trend, called XP or eXtreme Programming Waterfall design is the original software engineering methodology With waterfall design you pass through a set of fixed stages At each stage you have one task to perform Once you have completed that stage, you move to the next, and the next, until you have a complete, delivered piece of software The first stage is the requirements gathering stage, followed by the requirements analysis stage, and so forth until you come to the delivery and of course the maintenance stage afterwards You can move back to a previous stage once it is finished.

economists and quantitative analysts to go around saying that they can mathematically tame the markets; without Black Swans, there are no markets) But even after having been granted such generosity, theory would still present a shaky report card It turns out that economists are also lacking when it comes to predicting the small stuff, those regular market and economic movements that, while important, are not likely to cause panic-inducing tremors That is, it is not only that headline-grabbing crises are not being predicted, but even non-crisis-caliber, seminormal changes in key variables are consistently being widely mistargeted We are all familiar with how off-the-mark predictions of future GDP growth, in ation, unemployment, exchange rates, stock markets, or interest rates have traditionally been After all, there is a whole huge market (called the government bond market) that thrives on such unreliability.

12: Don t Forget Your Veggies!

Traders make bets on bonds based on their perception as to the future levels of variables such as those listed above, or on their perception as to their peers perceptions as to those future levels, which would be affected by currently available forecasts If such forecasts were invariably right, little money would be made in the market and activity would dry up It is thus tempting to conclude that one of the reasons for the bond market s extraordinary liquidity is that pros have little faith in economic predictions Long before the appearance of the Black Swan concept (and of economists-basher-in-chief Taleb) in the scene, economic forecasting and modeling had already received plenty of negative praise For instance, celebrity Harvard professor and author John Kenneth Galbraith once uttered that The only function of economic forecasting is to make astrology respectable.

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