Head First Data Analysis. A learner's guide to big numbers, statistics, and good decisions

Spis treści książki

• Head First Data Analysis
• Dedication
• A Note Regarding Supplemental Files
• Advance Praise for Head First Data Analysis
• Praise for other Head First books
• Author of Head First Data Analysis
• How to Use This Book: Intro
  • Who is this book for?
    • Who should probably back away from this book?
  • We know what youre thinking
  • We know what your brain is thinking
  • Metacognition: thinking about thinking
  • Heres what WE did
    • Heres what YOU can do to bend your brain into submission
  • Read Me
  • The technical review team
  • Acknowledgments
  • Safari Books Online
• 1. Introduction to Data Analysis: Break it down
  • 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
  • Acmes CEO has some feedback for you
  • Break the problem and data into smaller pieces
    • Divide the problem into smaller problems
    • Divide the data into smaller chunks
  • 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 CEOs 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 dont know
  • The CEO tells you what he doesnt know
  • Acme just sent you a huge list of raw data
  • Time to drill further into the data
  • General American Wholesalers confirms your impression
  • Heres what you did
  • Your analysis led your client to a brilliant decision
• 2. Experiments: Test your theories
  • Its a coffee recession!
  • The Starbuzz board meeting is in three months
  • The Starbuzz Survey
  • Always use the method of comparison
  • Comparisons are key for observational data
  • Could value perception be causing the revenue decline?
  • A typical customers thinking
  • Observational studies are full of confounders
  • How location might be confounding your results
  • Manage confounders by breaking the data into chunks
  • Its worse than we thought!
  • You need an experiment to say which strategy will work best
  • The Starbuzz CEO is in a big hurry
  • Starbuzz drops its prices
  • One month later...
  • Control groups give you a baseline
  • Not getting fired 101
  • Lets experiment for real!
  • One month later...
  • Confounders also plague experiments
  • Avoid confounders by selecting groups carefully
  • Randomization selects similar groups
  • Your experiment is ready to go
  • The results are in
  • Starbuzz has an empirically tested sales strategy
• 3. Optimization: Take it to the max
  • Youre 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
• 4. Data Visualization: Pictures make you smarter
  • New Army needs to optimize their website
  • The results are in, but the information designer is out
  • The last information designer submitted these three infographics
  • What data is behind the visualizations?
  • Show the data!
  • Heres some unsolicited advice from the last designer
  • Too much data is never your problem
  • Making the data pretty isnt your problem either
  • Data visualization is all about making the right comparisons
  • Your visualization is already more useful than the rejected ones
  • Use scatterplots to explore causes
  • The best visualizations are highly multivariate
  • Show more variables by looking at charts together
  • The visualization is great, but the web gurus not satisfied yet
  • Good visual designs help you think about causes
  • The experiment designers weigh in
  • The experiment designers have some hypotheses of their own
  • The client is pleased with your work
  • Orders are coming in from everywhere!
• 5. Hypothesis Testing: Say it aint so
  • Gimme some skin...
  • When do we start making new phone skins?
  • PodPhone doesnt want you to predict their next move
  • Heres everything we know
  • ElectroSkinnys 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 PodPhones 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 disconfirmation
  • You cant rule out all the hypotheses, but you can say which is strongest
  • You just got a picture message...
  • Its a launch!
• 6. Bayesian Statistics: Get past first base
  • The doctor has disturbing news
  • Lets take the accuracy analysis one claim at a time
  • How common is lizard flu really?
  • Youve been counting false positives
    • The opposite of a false positive is a true negative
  • All these terms describe conditional probabilities
  • You need to count
  • 1 percent of people have lizard flu
    • Watch out for the base rate fallacy
  • Your chances of having lizard flu are still pretty low
  • Do complex probabilistic thinking with simple whole numbers
  • Bayes rule manages your base rates when you get new data
  • You can use Bayes rule over and over
  • Your second test result is negative
  • The new test has different accuracy statistics
  • New information can change your base rate
  • What a relief!
• 7. Subjective Probabilities: Numerical belief
  • Backwater Investments needs your help
  • Their analysts are at each others throats
  • Subjective probabilities describe expert beliefs
  • Subjective probabilities might show no real disagreement after all
  • The analysts responded with their subjective probabilities
  • The CEO doesnt see what youre 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!
• 8. Heuristics: Analyze like a human
  • LitterGitters submitted their report to the city council
  • The LitterGitters have really cleaned up this town
  • The LitterGitters have been measuring their campaigns effectiveness
  • The mandate is to reduce the tonnage of litter
  • Tonnage is unfeasible to measure
  • Give people a hard question, and theyll answer an easier one instead
  • Littering in Dataville is a complex system
  • You cant build and implement a unified litter-measuring model
  • Heuristics are a middle ground between going with your gut and optimization
  • Use a fast and frugal tree
  • Is there a simpler way to assess LitterGitters success?
  • Stereotypes are heuristics
  • Your analysis is ready to present
  • Looks like your analysis impressed the city council members
• 9. Histograms: The shape of numbers
  • Your annual review is coming up
  • Going for more cash could play out in a bunch of different ways
  • Heres some data on raises
  • Histograms show frequencies of groups of numbers
  • Gaps between bars in a histogram mean gaps among the data points
  • Install and run R
  • Load data into R
  • R creates beautiful histograms
  • Make histograms from subsets of your data
  • Negotiation pays
  • What will negotiation mean for you?
• 10. Regression: Prediction
  • What are you going to do with all this money?
  • An analysis that tells people what to ask for could be huge
  • Behold... the Raise Reckoner!
  • Inside the algorithm will be a method to predict raises
  • Scatterplots compare two variables
  • A line could tell your clients where to aim
  • Predict values in each strip with the graph of averages
  • The regression line predicts what raises people will receive
  • The line is useful if your data shows a linear correlation
  • You need an equation to make your predictions precise
    • a represents the y-axis intercept
    • b represents the slope
  • Tell R to create a regression object
  • The regression equation goes hand in hand with your scatterplot
  • The regression equation is the Raise Reckoner algorithm
  • Your raise predictor didnt work out as planned...
• 11. Error: Err Well
  • Your clients are pretty ticked off
  • What did your raise prediction algorithm do?
  • The segments of customers
  • The guy who asked for 25% went outside the model
  • How to handle the client who wants a prediction outside the data range
  • The guy who got fired because of extrapolation has cooled off
  • Youve only solved part of the problem
  • What does the data for the screwy outcomes look like?
  • Chance errors are deviations from what your model predicts
  • Error is good for you and your client
  • Specify error quantitatively
  • Quantify your residual distribution with Root Mean Squared error
  • Your model in R already knows the R.M.S. error
  • Rs summary of your linear model shows your R.M.S. error
  • Segmentation is all about managing error
  • Good regressions balance explanation and prediction
  • Your segmented models manage error better than the original model
  • Your clients are returning in droves
• 12. Relational Databases: Can you relate?
  • The Dataville Dispatch wants to analyze sales
  • Heres the data they keep to track their operations
  • You need to know how the data tables relate to each other
  • A database is a collection of data with well-specified relations to each other
  • Trace a path through the relations to make the comparison you need
  • Create a spreadsheet that goes across that path
  • Your summary ties article count and sales together
  • Looks like your scatterplot is going over really well
  • Copying and pasting all that data was a pain
  • Relational databases manage relations for you
  • Dataville Dispatch built an RDBMS with your relationship diagram
  • Dataville Dispatch extracted your data using the SQL language
  • Comparison possibilities are endless if your data is in a RDBMS
  • Youre on the cover
• 13. Cleaning Data: Impose order
  • 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
  • Cleaning messy data is all about preparation
  • Once youre organized, you can fix the data itself
  • Use the # sign as a delimiter
  • Excel split your data into columns using the delimiter
  • Use SUBSTITUTE to replace the carat character
  • You cleaned up all the first names
  • The last name pattern is too complex for SUBSTITUTE
  • Handle complex patterns with nested text formulas
  • R can use regular expressions to crunch complex data patterns
  • The sub command fixed your last names
  • Now you can ship the data to your client
  • Maybe youre not quite done yet...
  • Sort your data to show duplicate values together
  • The data is probably from a relational database
  • Remove duplicate names
  • You created nice, clean, unique records
  • Head First Head Hunters is recruiting like gangbusters!
  • Leaving town...
  • Its been great having you here in Dataville!
• A. Leftovers: The Top Ten Things (we didnt cover)
  • #1: Everything else in statistics
  • #2: Excel skills
  • #3: Edward Tufte and his principles of visualization
  • #4: PivotTables
  • #5: The R community
  • #6: Nonlinear and multiple regression
  • #7: Null-alternative hypothesis testing
  • #8: Randomness
  • #9: Google Docs
  • #10: Your expertise
• B. Install R: Start R up!
  • Get started with R
• C. Install Excel Analysis Tools: The ToolPak
  • Install the data analysis tools in Excel
• Index
• About the Author
• Copyright

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