# Exploratory Data Analysis by John W. Tukey

By John W. Tukey

The technique during this introductory publication is that of casual research of the information. tools variety from plotting picture-drawing strategies to particularly complicated numerical summaries. numerous of the tools are the unique creations of the writer, and all may be performed both with pencil or aided by means of hand held calculator.

**Read Online or Download Exploratory Data Analysis PDF**

**Similar probability & statistics books**

**Stochastic PDEs and Kolmogorov equations in infinite dimensions: Lectures**

Kolmogorov equations are moment order parabolic equations with a finite or an enormous variety of variables. they're deeply hooked up with stochastic differential equations in finite or limitless dimensional areas. They come up in lots of fields as Mathematical Physics, Chemistry and Mathematical Finance.

**Random Networks for Communication: From Statistical Physics to Information Systems**

While is a random community (almost) attached? How a lot details can it hold? how are you going to discover a specific vacation spot in the community? and the way do you method those questions - and others - while the community is random? The research of conversation networks calls for a desirable synthesis of random graph idea, stochastic geometry and percolation concept to supply types for either constitution and data stream.

**Non-uniform random variate generation**

Thls textual content ls approximately one small fteld at the crossroads of statlstlcs, operatlons study and laptop sclence. Statistleians desire random quantity turbines to check and examine estlmators prior to uslng them ln genuine l! fe. In operatlons learn, random numbers are a key part ln ! arge scale slmulatlons.

- Continuous-Time Markov Chains: An Applications-Oriented Approach (Springer Series in Statistics)
- Markov Models With Covariate Dependence for Repeated Measures
- Runs and Scans with Applications
- Introduction to Time Series Analysis and Forecasting (Wiley Series in Probability and Statistics)
- Counting Processes and Survival Analysis

**Additional resources for Exploratory Data Analysis**

**Sample text**

It will be good to do this in terms of a few numbers that are easily understood, and to agree on what these numbers are, what they are called, how they are marked, how they are routinely written down, and how they are usefully and easily shown graphically! To do this well, we must limit our objectives. It would be wrong to expect a standard summary to reveal the unusual--no matter how important the unusual may be when it occurs. Things like the separation of the pattern into groupings-- illustrated by exhibit 10 of chapter l--ought not to show in an easy summary of routine form.

If we have 13 values, the 7th will be the median--and the 4th from each end a hinge. 8, one at each folding point. 8 where we have shown the count (marked #), the depth of the median (marked M), the depth of the hinges (marked H), and the depth of the extremes (always 1; needs no other mark) to the left of an inverted-U enclosure, and have written the corresponding values in the enclosure. We put the median in the middle, the upper values on one side, and the lower values on the other (which is which does not matter, and can change from time to time and from person to person).

Often, as in this example, the numbers tend to pile up on--and just after--each "l"--an example of what is sometimes called the "abnormal law of large numbers". exhibit 7 of chapter 1: arbitrary example Mixed-leaf stem-and-Ieaf applied to 25 values, some trailing Al THE VALUES 5, -52, -27, -83, 8, -14, -122, -110,112,58, -119,33,18, -52, -19, 12, -82,14, 25, -182, -40, 64, -56, 5, 13. J) pI PROBLEMS 7a) Make a "for looking at" stem-and-Ieaf of the same data. 7b) Make a stem-and-Ieaf of the same data using only one size of leaves.