Head first data analysis: [a learner's guide to big numbers, statistics, and good decisions]
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Sebastopol, CA
O'Reilly
2009
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| Schriftenreihe: | O'Reilly's head first series
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXXVIII, 445 S. Ill., graph. Darst. |
| ISBN: | 9780596153939 |
Internformat
MARC
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| 100 | 1 | |a Milton, Michael |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Head first data analysis |b [a learner's guide to big numbers, statistics, and good decisions] |c Michael Milton |
| 264 | 1 | |a Sebastopol, CA |b O'Reilly |c 2009 | |
| 300 | |a XXXVIII, 445 S. |b Ill., graph. Darst. | ||
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Datensatz im Suchindex
| _version_ | 1804140797559308288 |
|---|---|
| adam_text | Titel: Head first data analysis
Autor: Milton, Michael
Jahr: 2009
table of contents
Table of Contents (Smeary)
Intro xxvii
1 Introduction to Data Analysis: Break It Down 1
2 Experiments: Test Your Theories 37
3 Optimization: Take It to the Max 75
4 Data Visualization: Pictures Make You Smarter 111
5 Hypothesis Testing: Say It Ain t So 139
6 Bayesian Statistics: Get Past First Base 169
7 Subjective Probabilities: Xumerical Belief 191
8 Heuristics: Analyze Like a Human 225
9 Histograms: The Shape of Numbers 251
10 Regression: Prediction 279
11 Error: Err Well 315
12 Relational Databases: Can You Relate? 359
13 Cleaning Data: Impose Order 385
i Leftovers: The Top Ten Things (We Didn t Cover) 417
ii Install R: Start R Up! 427
iii Install Excel Analysis Tools: The ToolPak 431
Table of Contents (the real thing)
Intro
Your brain On data analysis. Here you are trying to learn something,
while here your brain is doing you a favor by making sure the learning doesn t stick.
Your brain s thinking, Better leave room for more important things, like which wild
animals to avoid and whether naked snowboarding is a bad idea. So how do you
trick your brain into thinking that your life depends on knowing data analysis?
Who is this book for? xxviii
We know what you re thinking xxix
Metacognition xxxi
Bend your brain into submission xxxiii
Read Me xxxiv
The technical review team xxxvi
Acknowledgments xxxvii
antents
1
Introduction t9 data analysis
Break it down
Data is everywhere.
Nowadays, everyone has to deal with mounds of data, whether they call
themselves data analysts or not. But people who possess a toolbox of data
analysis skills have a massive edge on everyone else, because they understand
what to do with all that stuff. They know how to translate raw numbers into
intelligence that drives real-world action. They know how to break down and
structure complex problems and data sets to get right to the heart of the problems
in their business.
Acme Cosmetics needs your help
The CEO wants data analysis to help increase sales
Data analysis is careful thinking about evidence
Define the problem
Your client will help you define your problem
Acme s CEO has some feedback for you
Break the problem and data into smaller pieces
Now take another look at what you know
Evaluate the pieces
Analysis begins when you insert yourself
Make a recommendation
Your report is ready
The CEO likes your work
An article just came across the wire
You let the CEO s beliefs take you down the wrong path
Your assumptions and beliefs about the world are your mental model
Your statistical model depends on your mental model
Mental models should always include what you don t know
The CEO tells you what he doesn t know
Acme just sent you a huge list of raw data
Time to drill further into the data
General American Wholesalers confirms your impression
Here s what you did
Your analysis led your client to a brilliant decisii
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table or contents
2
People have (ess
money
experiments
Test your theories
Can you show what you believe?
In a real empirical test? There s nothing like a good experiment to solve your problems
and show you the way the world really works. Instead of having to rely exclusively on
your observational data, a well-executed experiment can often help you make causal
connections. Strong empirical data will make your analytical judgments all the more
^ It s a coffee recession! 38
The Starbuzz board meeting is in three months 39
The Starbuzz Survey 41
Always use the method of comparison 42
Comparisons are key for observational data 43
Could value perception be causing the revenue decline? 44
A typical customer s thinking 46
Observational studies are full of confounders 47
How location might be confounding your results 48
Manage confounders by breaking the data into chunks 50
It s worse than we thought! 53
You need an experiment to say which strategy will work best 54
The Starbuzz CEO is in a big hurry 55
Starbuzz drops its prices 56
One month later... 57
Control groups give you a baseline 58
Not getting fired 101 61
Let s experiment a^jnuijbr real! 62
One month later... 63
Confounders also plague experiments 64
Avoid confounders by selecting groups carefully 65
Randomization selects similar groups 67
Randomness Exposed 68
Your experiment is ready to go 71
The results are in 72
Starbuzz has an empirically tested sales strategy 73
All otter stores
People think Starbuzz
is less of a value
Starbuzz sales
go down
3
Take it to the max
We all want more of something.
And we re always trying to figure out how to get it. If the things we want more of—
profit, money, efficiency, speed—can be represented numerically, then chances
are, there s an tool of data analysis to help us tweak our decision variables, which
will help us find the solution or optimal point where we get the most of what
we want. In this chapter, you ll be using one of those tools and the powerful
spreadsheet Solver package that implements it.
You re now in the bath toy game
Constraints limit the variables you control
Decision variables are things you can control
You have an optimization problem
Find your objective with the objective function
Your objective function
Show product mixes with your other constraints
Plot multiple constraints on the same chart
Your good options are all in the feasible region
Your new constraint changed the feasible region
Your spreadsheet does optimization
Solver crunched your optimization problem in a snap
Profits fell through the floor
Your model only describes what you put into it
Calibrate your assumptions to your analytical objectives
Watch out for negatively linked variables
Your new plan is working like a charm
Your assumptions are based on an ever-changing reality
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tcruit? ji ^v/f
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data Visualization
Pictures make you smarter
You need more than a table of numbers.
Your data is brilliantly complex, with more variables than you can shake a stick at.
Mulling over mounds and mounds of spreadsheets isn t just boring; it can actually be a
waste of your time. A clear, highly multivariate visualization can, in a small space, show
you the forest that you d miss for the trees if you were just looking at spreadsheets all
the time.
Home p»ee
New Army needs m optimize their website 112
The results are in, but the information designer is out 1 1 3
The last information designer submitted these three infbgraphies 1 14
What data is behind the visualizations? 1 15
Show the data! Ill)
Here s some unsolicited advice from the last designer 117
Too much data is never your problem 118
Making the data pretty isn t your problem either 1 19
Data visualization is all about making the right comparisons 120
Your visualization is already more useful than the rejected ones 123
Use scatterplots to explore causes 124
The best visualizations are highly multivariate 125
Show more variables by looking at charts together 126
The visualization is great, but the web guru s not satisfied yet 130
Good visual designs help you think about causes 131
The experiment designers weigh in 132
The experiment designers have some hypotheses of their own 135
The client is pleased witli your work 136
Orders are coming in from everywhere! 137
viii
(f contents
i testing
Say it ain t so
The world can be tricky to explain.
And it can be fiendishly difficult when you have to deal with complex,
heterogeneous data to anticipate future events. This is why analysts don t just
take the obvious explanations and assume them to be true: the careful reasoning
of data analysis enables you to meticulously evaluate a bunch of options so that
you can incorporate all the information you have into your models. You re about to
learn about falsification, an unintuitive but powerful way to do just that.
Gimme some skin...
When do we start making new phone skins?
PodPhone doesn t want you to predict their next move
Here s everything we know
ElectroSkinny s analysis does fit the data
ElectroSkinny obtained this confidential strategy memo
Variables can be negatively or positively linked
Causes in the real world are networked, not linear
Hypothesize PodPhone s options
You have what you need to run a hypothesis test
Falsification is the heart of hypothesis testing
Diagnosticity helps you find the hypothesis with the least disconfirmaiio
You can t rule out all the hypotheses, but you can say which is strongest
You just got a picture message...
It s a launch!
167
Wy
6
table of contents
statistics
Get past first base
You ll always be collecting new data.
And you need to make sure that every analysis you do incorporates the data you have
that s relevant to your problem. You ve learned how falsification can be used to deal
with heterogeneous data sources, but what about straight up probabilities? The
answer involves an extremely handy analytic tool called Bayes rule, which will help
you incorporate your base rates to uncover not-so-obvious insights with ever-changing
data.
The doctor has disturbing news 170
Let s take the accuracy analysis one claim at a time 173
How common is lizard flu really? 1 74
You ve been counting false positives 175
All these terms describe conditional probabilities 176
You need to count false positives, true positives, false negatives, and true negatives 177
1 percent of people have lizard flu 178
Your chances of having lizard flu arc still pretty low 181
Do complex probabilistic thinking with simple whole numbers 182
Bayes rule manages your base rates when you get new data 182
You can use Bayes rule over and over 183
Your second test result is negative 184
The new test has different accuracy statistics 185
New information can change your base rate 186
What a relief! 189
7
subjective probab
Numerical belief
Sometimes, it s a good idea to make up numbers.
Seriously. But only if those numbers describe your own mental states, expressing
your beliefs. Subjective probability is a straightforward way of injecting some real
rigor into your hunches, and you re about to see how. Along the way, you are going
to learn how to evaluate the spread of data using standard deviation and enjoy a
special guest appearance from one of the more powerful analytic tools you ve learned.
Backwater Investments needs your help
Their analysts are at each other s throats
Subjective probabilities describe expert beliefs
Subjective probabilities might show no real disagreement after all
The analysts responded with their subjective probabilities
The CEO doesn t see what you re up to
The CEO loves your work
The standard deviation measures how far points are from the average
You were totally blindsided by this news
Bayes rule is great for revising subjective probabilities
The CEO knows exactly what to do with this new information
Russian stock owners rejoice!
The mews about selling
the oil -fields.
You* £ wst analysis of
subjective probabilities.
Let s Hope
tte stodk
•«a rket joes
up|
je
badk up|
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Time
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table of contents
heuristics
Analyze like a human
The real world has more variables than you can handle.
There is always going to be data that you can t have. And even when you do have data
on most of the things you want to understand, optimizing methods are often elusive
and time consuming. Fortunately, most of the actual thinking you do in life is not
rational maximizing —it s processing incomplete and uncertain information with rules
of thumb so that you can make decisions quickly. What is really cool is that these rules
can actually work and are important (and necessary) tools for data analysts.
LitterGitters submitted their report to the city council 22(5
The LitterGitters have really cleaned up this town 227
The LitterGitters have been measuring their campaign s effectiveness 228
The mandate is to reduce the tonnage oi litter 229
Tonnage is unfeasible to measure 230
Give people a hard question, and they ll answer an easier one instead 231
Littering in Dataville is a complex system 232
You can t build and implement a unified litter-measuring model 233
Heuristics are a middle ground between going with your gut and optimization 236
Use a fast and frugal tree 239
Is there a simpler way to assess LitterGitters success? 240
Stereotypes are heuristics 244
Your analysis is ready to present 246
Looks like your analysis impressed the city council members 249
if contents
The shape of numbers
How much can a bar graph tell you?
There are about a zillion ways of showing data with pictures, but one of them is
special. Histograms, which are kind of similar to bar graphs, are a super-fast and
easy way to summarize data. You re about to use these powerful little charts to
measure your data s spread, variability, central tendency, and more. No matter
how large your data set is, if you draw a histogram with it, you ll be able to see
what s happening inside of it. And you re about to do it with a new, free, crazy-
powerful software tool.
Your annual review is coming up 252
Going for more cash could play out in a bunch of different ways 254
Here s some data on raises 255
Histograms show frequencies of groups of numbers 262
Gaps between bars in a histogram mean gaps among the data points 263
Install and run R 264
Load data into R 265
R creates beautiful histograms 266
Make histograms from subsets of your data 271
Negotiation pays 276
What will negotiation mean for you? 277
10
table of contents
regression
Prediction
Predict it.
Regression is an incredibly powerful statistical tool that, when used correctly, has the
ability to help you predict certain values. When used with a controlled experiment,
regression can actually help you predict the future. Businesses use it like crazy to help
them build models to explain customer behavior. You re about to see that the judicious
use of regression can be very profitable indeed.
What arc you going to do with all this money? 280
An analysis that tells people what to ask for could be huge 283
Behold... the Raise Reckoner! 284
Inside the algorithm will be a method to predict raises 286
Scatterplots compare two variables 292
A line could tell your clients where to aim 294
Predict values in each strip with the graph of averages 297
The regression line predicts what raises people will receive 298
The line is useful if your data shows a linear correlation 300
You need an equation to make your predictions precise 304
Tell R to create a regression object 306
The regression equation goes hand in hand with your scatterplot 309
The regression equation is the Raise Reckoner algorithm 310
Your raise predictor didn t work out as planned... 313
if contents
11
error
Err well
The world is messy.
So it should be no surprise that your predictions rarely hit the target squarely. But if
you offer a prediction with an error range, you and your clients will know not only
the average predicted value, but also how far you expect typical deviations from
that error to be. Every time you express error, you offer a much richer perspective
on your predictions and beliefs. And with the tools in this chapter, you ll also learn
about how to get error under control, getting it as low as possible to increase
confidence.
Your clients are pretty ticked off 316
What did your raise prediction algorithm do? 317
The segments of customers 318
The guy who asked for 25 % went outside the model 321
How to handle the client who wants a prediction outside the data range 322
The guy who got fired because of extrapolation has cooled off 327
You ve only solved part of the problem 328
What does the data for the screwy outcomes look like? 329
Chance errors are deviations from what your model predicts 33°
Error is good for you and your client 334
Chance Error Exposed 335
Specify error quantitatively 336
Quantify your residual distribution with Root Mean Squared error 337
Your model in R already knows the R.M.S. error 338
R s summary of your linear model shows your R.M.S. error 340
Segmentation is all about managing error 346
Good regressions balance explanation and prediction 35°
Your segmented models manage error better than the original model 352
Your clients are returning in droves 357
table of contents
12
relational databases
Can you relate?
How do you structure really, really multivariate data?
A spreadsheet has only two dimensions: rows and columns. And if you have a
bunch of dimensions of data, the tabular format gets old really quickly. In this
chapter, you re about to see firsthand where spreadsheets make it really hard
to manage multivariate data and learn how relational database management
systems make it easy to store and retrieve countless permutations of multivariate
data.
The Dataville Dispatch wants to analyze sales 360
Here s the data they keep to track their operations 361
You need to know how the data tables relate to each other 362
A database is a collection of data with well-specified relations to each other 365
Trace a path through the relations to make the comparison you need 366
Create a spreadsheet that goes across that path 366
Your summary ties article count and sales together 371
Looks like your scatterplot is going over really well 374
Copying and pasting all that data was a pain 375
Relational databases manage relations for you 376
Dataville Dispatch built an RDBMS with your relationship diagram 377
Dataville Dispatch extracted your data using the SQL language 379
Comparison possibilities are endless if your data is in a RDBMS 382
You re on the cover 383
of contents
3
cleaning
Impose order
Your data is useless...
.. .if it has messy structure. And a lot of people who collect data do a crummy job
of maintaining a neat structure. If your data s not neat, you can t slice it or dice it,
run formulas on it, or even really see it. You might as well just ignore it completely,
right? Actually, you can do better. With a clear vision of how you need it to look
and a few text manipulation tools, you can take the funkiest, craziest mess of
data and whip it into something useful.
Just got a client list from a defunct competitor * °
The dirty secret of data analysis ^
Head First Head Hunters wants the list for their sales team 388
Cleaning messy data is all about preparation ¦
Once you re organized, you can fix the data itself ™3
Use the # sign as a delimiter *
Excel split your data into columns using the delimiter 393
Use SUBSTITUTE to replace the carat character 3
You cleaned up all the first names
The last name pattern is too complex for SUBSTITUTE 402
Handle complex patterns with nested text formulas
R can use regular expressions to crunch complex data patterns 404
The sub command fixed your last names
Now you can ship the data to your client
Maybe you re not quite done yet...
Sort your data to show duplicate values together
The data is probably from a relational database
Remove duplicate names 413
You created nice, clean, unique records 414
Head First Head Hunters is recruiting like gangbusters!
Leaving town...
1
table of contents
leftovers
The Top Ten Things (we didn t cover)
You ve come a long way.
But data analysis is a vast and constantly evolving field, and there s so much left the
learn. In this appendix, we ll go over ten items that there wasn t enough room to cover
in this book but should be high on your list of topics to learn about next.
# 1: Everything else in statistics 11H
#2: Excel skills -11!)
#3: Edward Tuite and his principles of visuuli/iitiim 120
#4: PivotTables 121
#5: The R community 122
#6: Nonlinear and multiple regression 12!i
#7: Null-alternative hypothesis testing 121
#8: Randomness 12]
#9: Google Docs 125
# 10: Your expertise 12h
of contents
Install x
Start R up!
##
U Behind all that data-crunching power is enormous
complexity.
But fortunately, getting R installed and started is something you can accomplish in
just a few minutes, and this appendix is about to show you how to pull off your R
install without a hitch.
Get started with R 428
table of contents
Install xcel analysis tools
The ToolPak
mSome of the best features of Excel aren t installed by
default.
That s right, in order to run the optimization from Chapter 3 and the histograms from
Chapter 9, you need to activate the Solver and the Analysis ToolPak, two extensions
that are included in Excel by default but not activated without your initiative.
Install the data analysis tools in Exci-I 432
|
| any_adam_object | 1 |
| author | Milton, Michael |
| author_facet | Milton, Michael |
| author_role | aut |
| author_sort | Milton, Michael |
| author_variant | m m mm |
| building | Verbundindex |
| bvnumber | BV035833628 |
| classification_rvk | QH 232 SK 840 |
| ctrlnum | (OCoLC)634988953 (DE-599)BVBBV035833628 |
| discipline | Mathematik Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV035833628 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T22:05:41Z |
| institution | BVB |
| isbn | 9780596153939 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-018692111 |
| oclc_num | 634988953 |
| open_access_boolean | |
| owner | DE-M347 DE-11 DE-2070s DE-739 |
| owner_facet | DE-M347 DE-11 DE-2070s DE-739 |
| physical | XXXVIII, 445 S. Ill., graph. Darst. |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | O'Reilly |
| record_format | marc |
| series2 | O'Reilly's head first series |
| spelling | Milton, Michael Verfasser aut Head first data analysis [a learner's guide to big numbers, statistics, and good decisions] Michael Milton Sebastopol, CA O'Reilly 2009 XXXVIII, 445 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier O'Reilly's head first series Datenanalyse (DE-588)4123037-1 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf EXCEL (DE-588)4138932-3 gnd rswk-swf Datenanalyse (DE-588)4123037-1 s EXCEL (DE-588)4138932-3 s R Programm (DE-588)4705956-4 s 1\p DE-604 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018692111&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Milton, Michael Head first data analysis [a learner's guide to big numbers, statistics, and good decisions] Datenanalyse (DE-588)4123037-1 gnd R Programm (DE-588)4705956-4 gnd EXCEL (DE-588)4138932-3 gnd |
| subject_GND | (DE-588)4123037-1 (DE-588)4705956-4 (DE-588)4138932-3 |
| title | Head first data analysis [a learner's guide to big numbers, statistics, and good decisions] |
| title_auth | Head first data analysis [a learner's guide to big numbers, statistics, and good decisions] |
| title_exact_search | Head first data analysis [a learner's guide to big numbers, statistics, and good decisions] |
| title_full | Head first data analysis [a learner's guide to big numbers, statistics, and good decisions] Michael Milton |
| title_fullStr | Head first data analysis [a learner's guide to big numbers, statistics, and good decisions] Michael Milton |
| title_full_unstemmed | Head first data analysis [a learner's guide to big numbers, statistics, and good decisions] Michael Milton |
| title_short | Head first data analysis |
| title_sort | head first data analysis a learner s guide to big numbers statistics and good decisions |
| title_sub | [a learner's guide to big numbers, statistics, and good decisions] |
| topic | Datenanalyse (DE-588)4123037-1 gnd R Programm (DE-588)4705956-4 gnd EXCEL (DE-588)4138932-3 gnd |
| topic_facet | Datenanalyse R Programm EXCEL |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018692111&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT miltonmichael headfirstdataanalysisalearnersguidetobignumbersstatisticsandgooddecisions |