An edition of Exploratory data analysis
(1970)
Exploratory data analysis
by John Wilder Tukey
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Exploratory Data Analysis (Classic Version)
2019, Pearson Education Canada
in English
0134995457 9780134995458
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Exploratory data analysis, John W. Tukey
Apr 09, 1977, Addison-Wesley, Pearson
in English
0201076160 9780201076165
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Book Details
First Sentence
"Exploratory data analysis is detective work — — numerical detective work — — or counting detective work — — or graphical detective work."
Table of Contents
. <b>1 Scratching Down Numbers (stem-and-leaf)</b>, 1
Comments about the page index, 2
1A Quantitative detective work, 1
1B Pratical arithmetic, 3
1C Scratching down numbers, 6
1D Doing better with setm-and-leaf, 7
1E Using the right number of stems, 11
1F How to count by tallying, 16
1G What does it mean to "feel what the data are like"?, 19
1H How far have we come?
1K How to use stem-and-leaf to pick up additional information (optimal technique), 23
1P Additional problems, 25
. <b>2 Schematic Summaries (pictures and numbers)</b>, 27
2A Extremes and median, 29
2B Hinges and 5-number summaries, 32
2C Box-and-wisker plots, 39
2D Fences, and outside values, 43
2E Schematic plots, 47
2F Pros and cons; the Rayleigh example, 49
2G Eights, sisteenths, etc., 53
2H How far hve we come?, 55
. <b>3 Easy Re-Expression</b>, 57
3A Logarithms = logs, 59
3B Quick logs, 61
3C Comparisons of two batches 64
3D Quick roots and quick reciprocals, 69
3E Looking quickly, 79
3F Counted data, 83
3G Relation among powers and logs (optional), 86
3H How far have we come?, 92
3K How to think about logs (background), 93
3P Additional problems, 93
. <b>4 Effective Comparison (including well-chosen expression)</b>, 97
4A Alternative forms of display of summaries, 99
4B Comparing several batches (continued), 102
4C A more extensive example, 105
4D The meaning of comparison, 110
4E Adjustments, rough and exact, 110
4F Residuals, 113
4H How far have we come?, 115
4P Additional problems, 116
. <b>5 Plots of Relationship</b>, 125
5A How to plot <i>y</i> against <i>x</i>, 126
5B Looking at subtraction, 131
5C Subtracting straight lines, 135
5D Plotting the population of the U.S.A., 141
5E Plotting the ratio of births to deaths, 148
5F Untilting defines "tilt", 157
5H How far have we come?, 156
5P Additional problems, 157
. <b>6 Straight Out Plots (using three points)</b>, 169
6A Looking at three points, 171
6B Re-expressing <i>y</i> alone, 172
6C Re-expression <i>x</i> alone, 175
6D A breaking example, 181
6E The vapor pressure of H<sub>2</sub>O, 187
6F Re-expressing the second variable, 191
6G Wise change of origin as a preliminary, 193
6H How far have we come?, 197
6P Additional problems, 199
. <b>7 Smoothing Sequences</b>, 205
7A Medians of 3, 210
7B Eye resmoothing, 214
7C Looking ahead, 216
7D Copyong-on- - and more, usually, 221
7E Blurring the smooth--and setting the fences, 223
7F Splitting peaks and valleys, 227
7G Hanning, 231
7H How far have we come?, 235
. <b>7<sup>+</sup> Optional Sections for Chapter 7</b>, 237
7I Breaking a smooth, 237
7J Choice of expression, 247
7K A two-section example, 259
7M How much more may we have learned?, 264
. <b>8 Parallel and Wandering Schematic Plots</b>, 265
8A Parallel schematic plots, 265
8B Smoothing the cross-medians, 274
8C Smoothing broken hihges, 276
8D Dealing with the two questions, 279
8E Wandering semantic plots, 283
8F A more demanding example: Governor's salary and bank deposits, 287
8G Further questions/analysis in the example, 298
8H How far have we come?, 306
8I The need to smooth both coordinates (optional), 307
. <b>9 Delineations of Batches of Points</b>, 309
9A E-traces and D-traces, 309
9B Simple dileneation - - Twin Riyers again, 311
9C Reduced and schematic delineations, 313
9D What our schematic plots and delineations have missed, 319
9E Threee variables at once - - or more, 321
9H How far have we come?, 329
. <b>10 Using Two-Way Analysis</b>, 331
10A Two-way risiduals; row-PLUS-column analysis, 332
10B The row-PLUS-column fit, 337
10C Some points of technique, 343
10D Row-TIMES-column analysis, 344
10E Looking at row-PLUS-column fits and their residuals, 349
10F Fitting one more constant, 352
10G Converting PLUS to TIMES; re-expression, 358
10H How far have we come?, 360
. <b>11 Making Two-Way Analyses</b>, 362
11A Taking medians out, 363
11B Alternative organizations of the arithmetic, 372
11C Making the core of a two-way plot, 374
11D Going on with the residuals, 378
11E Coding risiduals; condensing fits and residuals, 382
11F We can combine!, 390
11G Guidance for expression, 396
11H How far have we come?, 399
. <b>11<sup>+</sup> Optional Sections for Chapters 10 and 11</b>, 401
11I Exploring beyond PLUS-one (extends Chapter 10), 401
11J Taking out any summary, 404
11K An example of re-expresssion - - city killings, 408
11L An unusual fit, 415
11M How much more may we have learned?, 419
. <b>12 Advanced Fits</b>, 420
12A PLUS-one fits, 421
12B Picutres for "-Plus-one" fits, 424
12C Making those pictures, 428
12D Sometimes we can have parallel-line plots, still, 431
12E More extended fits, 438
12F Simplification is sometimes possible, 438
12H How far have we come?, 441
. <b>13 Three-Way Fits</b>, 443
13A Three- and more-way analyses: Arrangement and tagging, 443
13B An analysis of the psychological example, 448
13C Making three-way analysis, 452
13D Three-way re-expression, 458
13E More about the example, 462
13H How far have we come?, 465
. <b>14 Looking in Two or More Ways at Batches of Points</b>, 466
14A Coordinates and level traces, 467
14B Different middle traces for the same slices, 470
14C An explanation, 475
14D Changing the slicing coordinate, 476
14E What matters?
14F Rematching and strength of relationship, 482
14H How far have we come?, 491
14I The ubiquity of medians (optional section), 492
. <b>15 Counted Fractions</b>, 494
15A Started counts and counted fractions, 496
15B Three matched scales for counted fractions, 498
15C Quicker calculations, 502
15D Examples where careful expression clearly pays off, 508
15E Double Folding — — the 2 x 2 case, 513
15F Double folding — — larger cases, 516
15G Easy froots and flogs with a slide rule (optional), 520
15H How far have we come?, 522
. <b>16 Better Smoothing</b>, 523
16A Reroughing, 523
16B Some examples, 526
16C If w want things still smother, 531
16D Further possibilities, 534
16H How far have we come?, 542
. <b>17 COUNTS in BIN after BIN</b>, 543
17A Root smooth and root rough, 453
17B Counts of basic counts, 550
17C Fitting to smoothed roots, 555
17D Corn borers, wheat prices, and Student's simulations, 561
17E Bins of unequal width, 570
17F Double roots, 576
17G Cautionary examples, 582
17H How far have we come?, 587
. <b>18 Product-Ratio Plots</b>, 588
18A Sizes and counts, 589
18B Product-ratio analysis, 594
18C Forcing the unusual to be noticed, 598
18D Comparisons between collections, 602
18E Looking at the smallest basic count, 604
18F When zeros are counted, 605
18G Under the microscope, 608
18H How far have we come?, 612
. <b>19 Shapes of Distributions</b>, 614
19A Looking at shapes of distribution, 616
19B The Gaussian reference, 623
19C Using letter values to look at shapes of distribution, 626
19D Pushback technique (optinal section), 637
19H How far have we come?, 644
. <b>20 Mathematical Distributions</b>, 646
20A Binnings vs. distributions, 648
20B Densities for distributions vs. densities for binnings, 651
20C Tables and pictures comparing two sets of shapes of distributions, 654
20H How far have we come?, 661
. <b>21 Postscript</b>, 662
21A Our relationship to the computer, 663
21B What as been omitted?, 664
21C How should the past chapters look different?, 665
21D What have we been introduced to?, 666
<b>Glossary</b>, 667
<b>Index to Reference Tables</b>, 677
<b>Frontpapers</b>
1 Break table for two-decimal logs
2 Break table for (square) roots
3 Main break table— —digits of negative reciprocals
<b>Rearpapers</b>
4 Pluralities, folded roots, folded logarithms
5 Value of log<sub><i>e</i></sub> √(count + 1/6)
6 Values of √(count + 1/6)
Edition Notes
On spine: EDA.
Includes index.
Classifications
The Physical Object
Edition Identifiers
Work Identifiers
First Sentence
"Exploratory data analysis is detective work — — numerical detective work — — or counting detective work — — or graphical detective work."
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