Florence Nightingale: pie chart

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Keeping Up With the Quants: Your Guide to Understanding and Using Analytics by Thomas H. Davenport, Jinho Kim

Black-Scholes formula, business intelligence, business process, call centre, computer age, correlation coefficient, correlation does not imply causation, Credit Default Swap, en.wikipedia.org, feminist movement, Florence Nightingale: pie chart, forensic accounting, global supply chain, Hans Rosling, hypertext link, invention of the telescope, inventory management, Jeff Bezos, Johannes Kepler, longitudinal study, margin call, Moneyball by Michael Lewis explains big data, Myron Scholes, Netflix Prize, p-value, performance metric, publish or perish, quantitative hedge fund, random walk, Renaissance Technologies, Robert Shiller, Robert Shiller, self-driving car, sentiment analysis, six sigma, Skype, statistical model, supply-chain management, text mining, the scientific method, Thomas Davenport

It may be useful to make such information available in an appendix to a report or presentation, but don’t let it get in the way of telling a good story with your data—and start with what your audience really needs to know. Historical Examples of Communicating Results, Good and Bad Presentation of quantitative results is a technique that has been used for a long time, and, as now, can successfully convince the intended audience or completely undermine the importance of those results. Let’s look at an example of each. Florence Nightingale: A Good Example of Communicating Results Florence Nightingale is widely known as the founder of the profession of nursing and a reformer of hospital sanitation methods, but she was also a very early user of quantitative methods. When Nightingale and thirty-eight volunteer nurses were sent in October 1854 to a British military hospital in Turkey during the Crimean War, she found terrible conditions in a makeshift hospital.

Xiao-Li Meng, “Statistics: Your Chance for Happiness (or Misery),” course description, http://www.stat.harvard.edu/Academics/invitation_chair_txt.html. 3. David Schmitt, “Tell a Story,” June 27, 2012, http://www.allanalytics.com/ author.asp?id=2092&doc_id=246428. 4. I. Bernard Cohen, The Triumph of Numbers: How Counting Shaped Modern Life (New York: W.W. Norton, 2006), chapter 9; “Florence Nightingale,” Wikipedia, http://en.wikipedia.org/wiki/Florence_Nightingale; P. Nuttall, “The Passionate Statistician,” Nursing Times 28 (1983): 25–27. 5. Gregor Mendel, “Experiments in Plant Hybridization,” http://www.mendelweb.org/; “Gregor Mendel,” Wikipedia, http://en.wikipedia.org/wiki/Gregor_Mendel; Seung Yon Rhee, Gregor Mendel, Access Excellence, http://www.accessexcellence.org/RC/AB/BC/Gregor_Mendel.php; “Mendel’s Genetics,” anthro.palomar.edu/mendel/mendel_1.htm; David Paterson, “Gregor Mendel,” www .zephyrus.co.uk/gregormendel.html; “Rocky Road: Gregor Mendel,” Strange Science, www.strangescience.net/mendel.htm; Wolf-Ekkehard Lönnig, “Johann Gregor Mendel: Why His Discoveries Were Ignored for 35 Years,” www.weloennig .de/mendel02.htm; “Gregor Mendel and the Scientific Milieu of His Discovery,” www.2iceshs.cyfronet.pl/2ICESHS_Proceedings/Chapter_10/R-2_Sekerak.pdf; “Mendelian Inheritance,” Wikipedia, http://en.wikipedia.org/wiki/Mendelian_ inheritance. 6.

People were shocked to find that the wounded soldiers were dying, rather than being cured, in the hospital. Eventually, death rates were sharply reduced, as shown in the data Nightingale systematically collected. The mortality rate continued to fall. When she returned to England in June of 1856 after the Crimean War ended, she found herself a celebrity and praised as a heroine. FIGURE 4-1 * * * Florence Nightingale’s diagram of the causes of mortality in the “Army in the East” The Areas of the blue, red, & black wedges are each measured from the centre as the common vertex The blue wedges measured from the centre of the circle represent area for area the deaths from Preventible or Mitigable Zymotic Diseases, the red wedges measured from the centre the deaths from wounds, & the black wedges measured from the centre the deaths from all other causes The black line across the red triangle in Nov 1854 marks the boundary of the deaths from all other causes during the month In October 1854, & April 1855, the black area coincides with the red, in January & February 1856, the blue coincides with the black The entire areas may be compared by following the blue, the red, & the black lines enclosing them * * * Nightingale became a Fellow of the Royal Statistical Society in 1859—the first woman to become a member—and an honorary member of the American Statistical Association in 1874.


pages: 357 words: 110,072

Trick or Treatment: The Undeniable Facts About Alternative Medicine by Edzard Ernst, Simon Singh

animal electricity, Barry Marshall: ulcers, Berlin Wall, correlation does not imply causation, false memory syndrome, Florence Nightingale: pie chart, germ theory of disease, John Snow's cholera map, Louis Pasteur, meta analysis, meta-analysis, placebo effect, profit motive, publication bias, randomized controlled trial, Ronald Reagan, Simon Singh, The Design of Experiments, the scientific method

At a time when it was considered radical merely to include data tables, she also drew multicoloured diagrams that would not look out of place in a modern boardroom presentation. She even invented an elaborate version of the pie chart, known as the polar area chart, which helped to illustrate her data. She realized that illustrating her statistics would be enormously helpful in selling her argument to politicians, who were usually not well versed in mathematics. In due course, Nightingale’s statistical studies spearheaded a revolution in army hospitals, because the Royal Commission’s report led to the establishment of an Army Medical School and a system of collecting medical records. In turn, this resulted in a careful monitoring of which conditions and treatments did and did not benefit patients. Today, Florence Nightingale is best known as the founder of modern nursing, having established a curriculum and training college for nurses.

Bloodletting, on the other hand, was very much a standard treatment, but the establishment eventually had to reject its own practice because it was undermined by evidence from trials. There is one episode from the history of medicine that illustrates particularly well how an evidence-based approach forces the medical establishment to accept the conclusions that emerge when medicine is put to the test. Florence Nightingale, the Lady with the Lamp, was a woman with very little reputation, but she still managed to win a bitter argument against the male-dominated medical establishment by arming herself with solid, irrefutable data. Indeed, she can be seen as one of the earliest advocates of evidence-based medicine, and she successfully used it to transform Victorian healthcare. Florence and her sister were born during an extended and very productive two-year-long Italian honeymoon taken by their parents William and Frances Nightingale.

Florence and her sister were born during an extended and very productive two-year-long Italian honeymoon taken by their parents William and Frances Nightingale. Florence’s older sister was born in 1819 and named Parthenope after the city of her birth – Parthenope being the Greek name for Naples. Then Florence was born in the spring of 1820, and she too was named after the city of her birth. It was expected that Florence Nightingale would grow up to live the life of a privileged English Victorian lady, but as a teenager she regularly claimed to hear God’s voice guiding her. Hence, it seems that her desire to become a nurse was the result of a ‘divine calling’. This distressed her parents, because nurses were generally viewed as being poorly educated, promiscuous and often drunk, but these were exactly the prejudices that Florence was determined to crush.


The Data Journalism Handbook by Jonathan Gray, Lucy Chambers, Liliana Bounegru

Amazon Web Services, barriers to entry, bioinformatics, business intelligence, carbon footprint, citizen journalism, correlation does not imply causation, crowdsourcing, David Heinemeier Hansson, eurozone crisis, Firefox, Florence Nightingale: pie chart, game design, Google Earth, Hans Rosling, information asymmetry, Internet Archive, John Snow's cholera map, Julian Assange, linked data, moral hazard, MVC pattern, New Journalism, openstreetmap, Ronald Reagan, Ruby on Rails, Silicon Valley, social graph, SPARQL, text mining, web application, WikiLeaks

According to Rogers, this helped to show the real number of students receiving free education, which was much higher than official numbers showed. Figure 1-9. Data journalism in the Guardian in 1821 (the Guardian) Another early example in Europe is Florence Nightingale and her key report, “Mortality of the British Army”, published in 1858. In her report to Parliament, she used graphics to advocate improvements in health services for the British army. The most famous is her “coxcomb,” a spiral of sections each representing deaths per month, which highlighted that the vast majority of deaths were from preventable diseases rather than bullets. Figure 1-10. Mortality of the British army by Florence Nightingale (image from Wikipedia) Data Journalism and Computer-Assisted Reporting At the moment there is a “continuity and change” debate going on around the label “data journalism” and its relationship with previous journalistic practices that employ computational techniques to analyze datasets.

Some of the most famous charts and graphs came out of the need to better explain dense tables of data. William Playfair was a Scottish polyglot who lived in the late 1700s to early 1800s. He singlehandedly introduced the world to many of the same charts and graphs we still use today. In his 1786 book, Commercial and Political Atlas, Playfair introduced the bar chart to clearly show the import and export quantities of Scotland in a new and visual way. He then went on to popularize the dreaded pie chart in his 1801 book Statistical Breviary. The need for these new forms of charts and graphs came out of commerce, but as time passed, others appeared and were used to save lives. In 1854 John Snow created his now famous “Cholera Map of London” by adding a small black bar over each address where an incident was reported. Over time, an obvious density of the outbreak could be seen and action taken to curb the problem.

Figure 6-20. 2012 Presidential Campaign Finance (the Guardian) Tableau has some good online tutorials for you to start with, at http://www.tableausoftware.com/learn/training. Note Tableau is designed for PCs, although a Mac version is in the works. Use a mirror such as parallels to make it work. Google Spreadsheet Charts You can access this tool at http://www.google.com/google-d-s/spreadsheets/. Figure 6-21. UK government spending and taxation (the Guardian) After something simple (like a bar or line chart, or a pie chart), you’ll find that Google spreadsheets (which you create from the documents bit of your Google account) can create some pretty nice charts—including the animated bubbles used by Hans Rosling’s Gapminder. Unlike the charts API, you don’t need to worry about code; it’s pretty similar to making a chart in Excel, in that you highlight the data and click the chart widget. The customization options are worth exploring too; you can change colors, headings, and scales.


Statistics in a Nutshell by Sarah Boslaugh

Antoine Gombaud: Chevalier de Méré, Bayesian statistics, business climate, computer age, correlation coefficient, experimental subject, Florence Nightingale: pie chart, income per capita, iterative process, job satisfaction, labor-force participation, linear programming, longitudinal study, meta analysis, meta-analysis, p-value, pattern recognition, placebo effect, probability theory / Blaise Pascal / Pierre de Fermat, publication bias, purchasing power parity, randomized controlled trial, selection bias, six sigma, statistical model, The Design of Experiments, the scientific method, Thomas Bayes, Vilfredo Pareto

So you must make your own decision based on context and convention; I will present the same BMI information in pie chart form (Figure 4-30), and you may be the judge of whether this is a useful way to present the data. Note that this is a single pie chart, showing one year of data, but other options are available, including side-by-side charts (to facilitate comparison of the proportions of different groups) and exploded sections (to show a more detailed breakdown of categories within a segment). Figure 4-30. Pie chart showing BMI distribution for freshmen entering in 2005 Florence Nightingale and Statistical Graphics Most people are at least vaguely familiar with Florence Nightingale’s role in establishing nursing as a profession and with her heroic efforts to improve hygiene and the quality of nursing provided to British soldiers during the Crimean War. Fewer are aware of her contributions to statistical graphics, including her effective use of graphs and charts to communicate medical information.

It is immediately clear that the proportion of underweight students has declined, and the proportion of overweight and obese students has increased over time. Pie Charts The familiar pie chart presents data in a manner similar to the stacked bar chart: it shows graphically what proportion each part occupies of the whole. Pie charts, like stacked bar charts, are most useful when there are only a few categories of information and the differences among those categories are fairly large. Many people have particularly strong opinions about pie charts, and although pie charts are still commonly used in some fields, they have also been aggressively denounced in others as uninformative at best and potentially misleading at worst. So you must make your own decision based on context and convention; I will present the same BMI information in pie chart form (Figure 4-30), and you may be the judge of whether this is a useful way to present the data.

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Charts, Pie Charts, Pareto Charts–Pareto Charts, The Stem-and-Leaf Plot, The Boxplot–The Boxplot, The Histogram–Bivariate Charts, Bivariate Charts, Scatterplots–Scatterplots, Line Graphs–Line Graphs, Scatterplots–Relationships Between Continuous Variables about, Graphic Methods–Frequency Tables, Graphic Methods–Frequency Tables bar charts, Bar Charts–Pie Charts bivariate charts, Bivariate Charts, Scatterplots–Scatterplots, Line Graphs–Line Graphs, Scatterplots–Relationships Between Continuous Variables about, Bivariate Charts line graphs, Line Graphs–Line Graphs scatterplots, Scatterplots–Scatterplots, Scatterplots–Relationships Between Continuous Variables boxplot, The Boxplot–The Boxplot frequency tables, Frequency Tables–Frequency Tables histogram, The Histogram–Bivariate Charts Pareto charts, Pareto Charts–Pareto Charts pie charts, Pie Charts stem-and-leaf plot, The Stem-and-Leaf Plot graphical power calculator, Power Analysis graphical presentation of data, critiquing in articles, 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Incidence Incidence Rate (IR), Prevalence and Incidence independent samples (two-sample) t-test, The Independent Samples t-Test–Confidence Interval for the Independent Samples t-Test, Confidence Interval for a Proportion independent trials, Independence independent variables, Independent and Dependent Variables–Independent and Dependent Variables, Glossary of Statistical Terms definition of, Glossary of Statistical Terms dependent variables and, Independent and Dependent Variables–Independent and Dependent Variables index numbers, Index Numbers–Index Numbers, Glossary of Statistical Terms index of discrimination, Item Analysis index of temporal stability, Reliability indirect standardization, Crude, Category-Specific, and Standardized Rates–Crude, Category-Specific, and Standardized Rates inferential statistics, Inferential Statistics–Inferential Statistics, Inferential Statistics–Inferential Statistics, Inferential Statistics, Probability Distributions–The Binomial Distribution, Independent and Dependent Variables–Independent and Dependent Variables, Populations and Samples–Probability Sampling, The Central Limit Theorem–The Central Limit Theorem, Hypothesis Testing, Confidence Intervals–Confidence Intervals, p-values–p-values, The Z-Statistic–The Z-Statistic, Data Transformations–Data Transformations, Populations and Samples, Extrapolation and Trends–Linear regression about, Inferential Statistics–Inferential Statistics central limit theorem, The Central Limit Theorem–The Central Limit Theorem confidence intervals, Confidence Intervals–Confidence Intervals data transformations, Data Transformations–Data Transformations hypothesis testing, Hypothesis Testing incorrect use of tests in inferential statistics, Extrapolation and Trends–Linear regression independent variables and dependent variables, Independent and Dependent Variables–Independent and Dependent Variables mean in, Inferential Statistics p-values, p-values–p-values populations and samples, Populations and Samples–Probability Sampling probability distributions in, Probability Distributions–The Binomial Distribution vs. descriptive statistics, Inferential Statistics–Inferential Statistics, Populations and Samples Z-statistic, The Z-Statistic–The Z-Statistic information bias, Information Bias–Information Bias, Glossary of Statistical Terms information, converting data into, Basic Concepts of Measurement informative censoring, Bias in Sample Selection and Retention interaction effects, Basic Vocabulary interaction variable, Glossary of Statistical Terms intercept, Graphing Equations intermediate response variable, Specifying Response Variables internal consistency, Glossary of Statistical Terms internal consistency reliability, Reliability internal consistency, measures of, Measures of Internal Consistency–Coefficient Alpha interquartile range, The Range and Interquartile Range–The Range and Interquartile Range, Glossary of Statistical Terms interrupted time series, Quasi-Experimental Studies intersection, Intersection, Intersection of independent events, Intersection of nonindependent events of independent events, Intersection of independent events of nonindependent events, Intersection of nonindependent events of simple events, Intersection interval data, Interval Data, Glossary of Statistical Terms about, Interval Data definition of, Glossary of Statistical Terms interval estimates, Confidence Intervals interviewer bias, Information Bias–Information Bias invariant difficulty, Item Response Theory investigations, checklist for statistics based, Quick Checklist–Quick Checklist item analysis, Item Analysis–Item Analysis Item Characteristic Curve (ICC), Item Response Theory item difficulty (signified as p), Test Construction–Test Construction, Item Analysis item discrimination, Item Analysis Item Response Theory (IRT), Item Response Theory–Item Response Theory J joint frequencies, The Chi-Square Test, The Risk Ratio journal clubs, presenting at, Linear regression journals, Writing for a Professional Journal–Writing the Article, Writing for a Professional Journal–The Peer Review Process, The Peer Review Process–The Peer Review Process, Common Problems–Common Problems, Quick Checklist–Quick Checklist, Issues in Research Design–The Power of Coincidence, Descriptive Statistics–Extrapolation and Trends, Extrapolation and Trends–Linear regression checklist for statistics based investigations, Quick Checklist–Quick Checklist common problems in articles, Common Problems–Common Problems critiquing descriptive statistics, Descriptive Statistics–Extrapolation and Trends incorrect use of tests in inferential statistics, Extrapolation and Trends–Linear regression issues in research design, Issues in Research Design–The Power of Coincidence peer review process, The Peer Review Process–The Peer Review Process writing for, Writing for a Professional Journal–Writing the Article, Writing for a Professional Journal–The Peer Review Process K Kaiser normalization, Factor Analysis kappa (kappa coefficient), Measures of Agreement–Measures of Agreement Kendall, Maurice, Ordinal Variables–Ordinal Variables Kendall’s tau-a, Ordinal Variables, Ordinal Variables Kendall’s tau-b, Ordinal Variables–Ordinal Variables Kendall’s tau-c, Ordinal Variables Knight, William, binomial distribution probability tables, The Binomial Distribution–The Binomial Distribution Kolmogorov–Smirnov test, Data Transformations–Data Transformations Kruskal-Wallis H test, Kruskal-Wallis H Test–Kruskal-Wallis H Test Kuder-Richardson formulas, Coefficient Alpha, Coefficient Alpha L lag, Time Series large-sample Z test for proportions, Proportions: The Large Sample Case–Proportions: The Large Sample Case Laspeyres index, Index Numbers–Index Numbers Latin square, in experimental design, Blocking and the Latin Square LDFs (Linear Discriminant Functions), Discriminant Function Analysis Levene’s test, Unequal Variance t-Test Likert scale, Exercises, The Likert and Semantic Differential Scales, Glossary of Statistical Terms Likert, Rensis, The Likert and Semantic Differential Scales line graphs, Line Graphs–Line Graphs linear algebra, Relationships Between Continuous Variables Linear Discriminant Functions (LDFs), Discriminant Function Analysis linear equations, The General Linear Model, Graphing Equations linear inequalities, Linear Inequalities–Linear Inequalities linear regression, Linear Regression–Linear Regression, Assumptions–Assumptions, Calculating Simple Regression by Hand–Calculating Simple Regression by Hand, Multiple Regression Models–Multiple Regression Models, Multiple Regression Models–Multiple Regression Models, Multiple Regression Models, Multiple Regression Models–Multiple Regression Models, Multiple Regression Models–Multiple Regression Models, Multiple Regression Models–Multiple Regression Models, Multiple Regression Models, Multiple Regression Models–Multiple Regression Models, Multiple Regression Models–Multiple Regression Models, Dummy Variables–Dummy Variables, Methods for Building Regression Models–Backward removal, Logistic Regression–Converting Logits to Probabilities, Logistic Regression, Multinomial Logistic Regression–Multinomial Logistic Regression, Polynomial Regression–Polynomial Regression, Polynomial Regression–Polynomial Regression, Polynomial Regression–Polynomial Regression, Overfitting–Overfitting, Linear regression about, Linear Regression–Linear Regression arbitrary curve-fitting, Overfitting–Overfitting assumptions, Assumptions–Assumptions calculating by hand, Calculating Simple Regression by Hand–Calculating Simple Regression by Hand cubic regression model, Polynomial Regression–Polynomial Regression logistic regression, Logistic Regression–Converting Logits to Probabilities logit outcome variable, Logistic Regression multinomial logistic regression, Multinomial Logistic Regression–Multinomial Logistic Regression multiple, Multiple Regression Models–Multiple Regression Models, Multiple Regression Models–Multiple Regression Models, Multiple Regression Models, Multiple Regression Models–Multiple Regression Models, Multiple Regression Models–Multiple Regression Models, Multiple Regression Models–Multiple Regression Models, Multiple Regression Models, Multiple Regression Models–Multiple Regression Models, Multiple Regression Models–Multiple Regression Models, Dummy Variables–Dummy Variables, Methods for Building Regression Models–Backward removal about, Multiple Regression Models–Multiple Regression Models adding interaction term, Multiple Regression Models–Multiple Regression Models assumptions, Multiple Regression Models creating a correlation matrix, Multiple Regression Models–Multiple Regression Models dummy variables, Dummy Variables–Dummy Variables methods for building regression models, Methods for Building Regression Models–Backward removal modeling principles, Multiple Regression Models–Multiple Regression Models regression equation for data, Multiple Regression Models–Multiple Regression Models results for individual predictors, Multiple Regression Models–Multiple Regression Models standardized coefficients, Multiple Regression Models variables in model, Multiple Regression Models–Multiple Regression Models polynomial regression, Polynomial Regression–Polynomial Regression quadratic, Polynomial Regression–Polynomial Regression violations of assumptions of, Linear regression literature review, Writing the Article, Evaluating the Whole Article critiquing in articles, Evaluating the Whole Article writing, Writing the Article Little, Donald B., String and Numeric Data–Missing Data local independence assumption, Item Response Theory logarithms (log), Properties of Roots–Properties of Roots, Solving Equations about, Properties of Roots–Properties of Roots in solving equations, Solving Equations logistic regression, Logistic Regression–Converting Logits to Probabilities logit outcome variable, Logistic Regression, Converting Logits to Probabilities–Converting Logits to Probabilities M Mahalanobis distance, Cluster Analysis main effects, Basic Vocabulary Manhattan distance, Cluster Analysis Mann-Whitney U test, The Wilcoxon Rank Sum Test Mantel-Haenszel (MH) common odds ratio, Confounding, Stratified Analysis, and the Mantel-Haenszel Common Odds Ratio–Confounding, Stratified Analysis, and the Mantel-Haenszel Common Odds Ratio marginal frequencies, The Risk Ratio marginals, The Chi-Square Test matching, Confounding, Stratified Analysis, and the Mantel-Haenszel Common Odds Ratio The Mathematics of Games and Gambling (Packel), Closing Note: The Connection between Statistics and Gambling maturation bias, Blocking and the Latin Square maximax decision making procedure, Minimax, Maximax, and Maximin–Minimax, Maximax, and Maximin, Glossary of Statistical Terms maximin decision making procedure, Minimax, Maximax, and Maximin–Minimax, Maximax, and Maximin, Glossary of Statistical Terms McNemar’s chi-square test, McNemar’s Test for Matched Pairs–McNemar’s Test for Matched Pairs mean, About Formulas, Inferential Statistics, The Mean–The Mean, The Variance and Standard Deviation, Glossary of Statistical Terms definition of, Glossary of Statistical Terms formula for, About Formulas in descriptive statistics, The Mean–The Mean in inferential statistics, Inferential Statistics sum of deviations from, The Variance and Standard Deviation mean rank, The Wilcoxon Rank Sum Test measurement, Basic Concepts of Measurement–Proxy Measurement, Measurement, Random and Systematic Error–Random and Systematic Error, Reliability and Validity–Triangulation about, Measurement random errors vs. systematic errors, Random and Systematic Error–Random and Systematic Error reliability and validity, Reliability and Validity–Triangulation types of, Basic Concepts of Measurement–Proxy Measurement measurement bias, types of, Measurement Bias–Information Bias measurement error, Classical Test Theory: The True Score Model measures of central tendency, Inferential Statistics, Measures of Central Tendency, The Mean–The Mean, The Mean–The Mean, The Mean–The Mean, The Median–The Median, The Mode–Comparing the Mean, Median, and Mode, Comparing the Mean, Median, and Mode–Comparing the Mean, Median, and Mode, Measures of Central Tendency–Measures of Central Tendency about, Measures of Central Tendency critiquing choice in article of, Measures of Central Tendency–Measures of Central Tendency in descriptive statistics, The Mean–The Mean, The Mean–The Mean mean, The Mean–The Mean, The Mean–The Mean mean, Inferential Statistics, The Mean–The Mean in descriptive statistics, The Mean–The Mean in inferential statistics, Inferential Statistics median, The Median–The Median, Comparing the Mean, Median, and Mode–Comparing the Mean, Median, and Mode vs. median and mode, Comparing the Mean, Median, and Mode–Comparing the Mean, Median, and Mode mode in, The Mode–Comparing the Mean, Median, and Mode measures of disease frequency, Measures of Disease Frequency measures of dispersion, Measures of Dispersion–Measures of Dispersion, The Range and Interquartile Range–The Range and Interquartile Range, The Variance and Standard Deviation–The Variance and Standard Deviation about, Measures of Dispersion–Measures of Dispersion range and interquartile range, The Range and Interquartile Range–The Range and Interquartile Range variance and standard deviation, The Variance and Standard Deviation–The Variance and Standard Deviation measures of internal consistency, Measures of Internal Consistency–Coefficient Alpha median, The Median–The Median, Glossary of Statistical Terms median test, The Median Test–The Median Test medical and epidemiological statistics, Medical and Epidemiological Statistics, Measures of Disease Frequency, Ratio, Proportion, and Rate–Ratio, Proportion, and Rate, Prevalence and Incidence–Prevalence and Incidence, Prevalence and Incidence–Prevalence and Incidence, Prevalence and Incidence, Crude, Category-Specific, and Standardized Rates–Crude, Category-Specific, and Standardized Rates, Crude, Category-Specific, and Standardized Rates–Crude, Category-Specific, and Standardized Rates, Crude, Category-Specific, and Standardized Rates–Crude, Category-Specific, and Standardized Rates, The Risk Ratio–Attributable Risk, Attributable Risk Percentage, and Number Needed to Treat, The Odds Ratio–The Odds Ratio, Confounding, Stratified Analysis, and the Mantel-Haenszel Common Odds Ratio–Confounding, Stratified Analysis, and the Mantel-Haenszel Common Odds Ratio, Confounding, Stratified Analysis, and the Mantel-Haenszel Common Odds Ratio–Confounding, Stratified Analysis, and the Mantel-Haenszel Common Odds Ratio, Confounding, Stratified Analysis, and the Mantel-Haenszel Common Odds Ratio–Confounding, Stratified Analysis, and the Mantel-Haenszel Common Odds Ratio, Power Analysis–Power Analysis about, Medical and Epidemiological Statistics category-specific rates, Crude, Category-Specific, and Standardized Rates–Crude, Category-Specific, and Standardized Rates confounding, Confounding, Stratified Analysis, and the Mantel-Haenszel Common Odds Ratio–Confounding, Stratified Analysis, and the Mantel-Haenszel Common Odds Ratio crude rate, Crude, Category-Specific, and Standardized Rates–Crude, Category-Specific, and Standardized Rates incidence, Prevalence and Incidence–Prevalence and Incidence Mantel-Haenszel (MH) common odds ratio, Confounding, Stratified Analysis, and the Mantel-Haenszel Common Odds Ratio–Confounding, Stratified Analysis, and the Mantel-Haenszel Common Odds Ratio measures of disease frequency, Measures of Disease Frequency odds ratio, The Odds Ratio–The Odds Ratio power analysis, Power Analysis–Power Analysis prevalence, Prevalence and Incidence–Prevalence and Incidence, Prevalence and Incidence ratio, proportion, and rate, Ratio, Proportion, and Rate–Ratio, Proportion, and Rate risk ratio, The Risk Ratio–Attributable Risk, Attributable Risk Percentage, and Number Needed to Treat standardization, Crude, Category-Specific, and Standardized Rates–Crude, Category-Specific, and Standardized Rates stratified analysis, Confounding, Stratified Analysis, and the Mantel-Haenszel Common Odds Ratio–Confounding, Stratified Analysis, and the Mantel-Haenszel Common Odds Ratio methods for building regression models, Methods for Building Regression Models–Methods for Building Regression Models, Methods for Building Regression Models–Methods for Building Regression Models, Methods for Building Regression Models automated, Methods for Building Regression Models blocking, Methods for Building Regression Models–Methods for Building Regression Models stepwise, Methods for Building Regression Models–Methods for Building Regression Models about, Methods for Building Regression Models–Methods for Building Regression Models methods section, writing, Writing the Article Microsoft Access, for data management, Spreadsheets and Relational Databases Microsoft Excel, Graphic Methods, Bar Charts, The Rectangular Data File, Spreadsheets and Relational Databases, Microsoft Excel–Microsoft Excel bar charts in, Bar Charts for data management, Spreadsheets and Relational Databases graphing in, Graphic Methods rectangular data file in, The Rectangular Data File using for statistical package, Microsoft Excel–Microsoft Excel minimax decision making procedure, Minimax, Maximax, and Maximin–Minimax, Maximax, and Maximin, Glossary of Statistical Terms Minitab, Minitab–Minitab Minnesota Multiphase Personality Inventory- II (MMPI-II), Standardized Scores misusing statistics, The Misuse of Statistics–The Misuse of Statistics MMPI-II (Minnesota Multiphase Personality Inventory- II), Standardized Scores mode, The Mode, Glossary of Statistical Terms Motorola, Quality Improvement MTMM (multitrait, multimethod matrix), Triangulation multicollinearity, Multiple Regression Models multinomial logistic regression, Multinomial Logistic Regression–Multinomial Logistic Regression multiple linear regression, Multiple Regression Models–Multiple Regression Models, Multiple Regression Models–Multiple Regression Models, Multiple Regression Models, Multiple Regression Models–Multiple Regression Models, Multiple Regression Models–Multiple Regression Models, Multiple Regression Models–Multiple Regression Models, Multiple Regression Models, Multiple Regression Models–Multiple Regression Models, Multiple Regression Models–Multiple Regression Models, Dummy Variables–Dummy Variables, Methods for Building Regression Models–Backward removal about, Multiple Regression Models–Multiple Regression Models adding interaction term, Multiple Regression Models–Multiple Regression Models assumptions, Multiple Regression Models creating a correlation matrix, Multiple Regression Models–Multiple Regression Models dummy variables, Dummy Variables–Dummy Variables methods for building regression models, Methods for Building Regression Models–Backward removal modeling principles, Multiple Regression Models–Multiple Regression Models regression equation for data, Multiple Regression Models–Multiple Regression Models results for individual predictors, Multiple Regression Models–Multiple Regression Models standardized coefficients, Multiple Regression Models variables in model, Multiple Regression Models–Multiple Regression Models multiple-forms (parallel-forms) reliability, Reliability, Reliability of a Composite Test multiple-occasions reliability (test-retest reliability), Reliability, Reliability of a Composite Test multivariate, Bivariate Charts mutual exclusive events, Mutual Exclusivity N Naperian logarithms, Properties of Roots National Institute of Standards and Technology, Unequal Variance t-Test Engineering Statistics Handbook, Unequal Variance t-Test National Institute of Standards and Technology (U.S.


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The Formula: How Algorithms Solve All Our Problems-And Create More by Luke Dormehl

3D printing, algorithmic trading, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, big data - Walmart - Pop Tarts, call centre, Cass Sunstein, Clayton Christensen, commoditize, computer age, death of newspapers, deferred acceptance, disruptive innovation, Edward Lorenz: Chaos theory, Erik Brynjolfsson, Filter Bubble, Flash crash, Florence Nightingale: pie chart, Frank Levy and Richard Murnane: The New Division of Labor, Google Earth, Google Glasses, High speed trading, Internet Archive, Isaac Newton, Jaron Lanier, Jeff Bezos, job automation, John Markoff, Kevin Kelly, Kodak vs Instagram, lifelogging, Marshall McLuhan, means of production, Nate Silver, natural language processing, Netflix Prize, Panopticon Jeremy Bentham, pattern recognition, price discrimination, recommendation engine, Richard Thaler, Rosa Parks, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley startup, Slavoj Žižek, social graph, speech recognition, Steve Jobs, Steven Levy, Steven Pinker, Stewart Brand, the scientific method, The Signal and the Noise by Nate Silver, upwardly mobile, Wall-E, Watson beat the top human players on Jeopardy!, Y Combinator

Guerry and Quetelet were translated into a number of different languages and widely reviewed. The Westminster Review—an English magazine founded by Utilitarians John Stuart Mill and Jeremy Bentham—devoted a particularly large amount of space to Guerry’s book, which it praised for being of “substantial interest and importance.” Charles Darwin read Quetelet’s work, as did Fyodor Dostoyevsky (twice), while no less a social reformer than Florence Nightingale based her statistical methods upon his own.13 Nightingale later gushingly credited Quetelet’s findings with “teaching us . . . the laws by which our Moral Progress is to be attained.”14 In all, Guerry and Quetelet’s work showed that human beings were beginning to be understood—not as free-willed, self-determining creatures able to do anything that they wanted, but as beings whose actions were determined by biological and cultural factors.

A Primer on Crime and Delinquency Theory (Belmont, Calif.: Wadsworth, 2001). 11 Hacking, Ian. The Taming of Chance (Cambridge, UK; New York: Cambridge University Press, 1990). 12 Rafter, Nicole. The Origins of Criminology: A Reader (New York: Routledge, 2009). 13 Mlodinow, Leonard. The Drunkard’s Walk: How Randomness Rules Our Lives (New York: Pantheon Books, 2008). 14 Belt, Elmer, and Louise Darling. Elmer Belt Florence Nightingale Collection (San Francisco: Internet Archive, 2009). 15 Danzigera, Shai, Jonathan Levav and Liora Avnaim-Pesso. “Extraneous Factors in Judicial Decisions.” PNAS, vol. 108, no. 17, April 26, 2011. pnas.org/content/108/17/6889.full. 16 Markoff, John. “Armies of Expensive Lawyers, Replaced by Cheaper Software.” New York Times, March 4, 2011. nytimes.com/2011/03/05/science/05legal.html. 17 Lev-Ram, Michal.

It was a friend of Conboy’s wife who eventually convinced him to open up the app for public use. Giving it the name Bedpost, and the tagline “Ever wonder how often you get busy?,” the app advises users to “simply log in after every time [they] have sex and fill out a few simple fields. Pretty soon, you’ll have a rolling history of your sex life on which to reflect.”31 This mass of data can be visualized in a variety of forms—including pie charts and scatter plots—with heat maps showing different intensities of color based on the quantity and quality of sex a particular user is having. “The amount of data you can attach to sexual activity is uncapped,” Conboy says. “For instance, if you’re on your phone, there is no reason you can’t record the GPS data. You might be having sex all over the world and it could be fun to look back at all that information.”