East Baton Rouge Parish Library

Translating statistics to make decisions, a guide for the non-statistician, Victoria Cox

eng

Includes bibliographical references

illustrations

no index present

non fiction

Translating statistics to make decisions

bibliographydictionaries

1975486975

Victoria Cox

a guide for the non-statistician

Examine and solve the common misconceptions and fallacies that non-statisticians bring to their interpretation of statistical results. Explore the many pitfalls that non-statisticians--and also statisticians who present statistical reports to non-statisticians--must avoid if statistical results are to be correctly used for evidence-based business decision making. Victoria Cox, senior statistician at the United Kingdom's Defence Science and Technology Laboratory (Dstl), distills the lessons of her long experience presenting the actionable results of complex statistical studies to users of widely varying statistical sophistication across many disciplines: from scientists, engineers, analysts, and information technologists to executives, military personnel, project managers, and officials across UK government departments, industry, academia, and international partners. The author shows how faulty statistical reasoning often undermines the utility of statistical results even among those with advanced technical training. Translating Statistics into Better Decisions teaches statistically naive readers enough about statistical questions, methods, models, assumptions, and statements that they will be able to extract the practical message from statistical reports and better constrain what conclusions cannot be made from the results. To non-statisticians with some statistical training, this book offers brush-ups, reminders, and tips for the proper use of statistics and solutions to common errors. To fellow statisticians, the author demonstrates how to present statistical output to non-statisticians to ensure that the statistical results are correctly understood and properly applied to real-world tasks and decisions. The book avoids algebra and proofs, but it does supply code written in R for those readers who are motivated to work out examples. Pointing along the way to instructive examples of statistics gone awry, Translating Statistics into Better Decisions walks readers through the typical course of a statistical study, progressing from the experimental design stage through the data collection process, exploratory data analysis, descriptive statistics, uncertainty, hypothesis testing, statistical modelling and multivariate methods, to graphs suitable for final presentation. The steady focus throughout the book is on how to turn the mathematical artefacts and specialist jargon that are second nature to statisticians into plain English for corporate customers and stakeholders. The final chapter neatly summarizes the book's lessons and insights for accurately communicating statistical reports to the non-statisticians who commission and act on them. Readers will " Recognize and avoid common errors and misconceptions that cause statistical studies to be misinterpreted and misused by non-statisticians in organizational settings " Gain a practical understanding of the methods, processes, capabilities, and caveats of statistical studies to improve the application of statistical data to business decisions " See how to code statistical solutions in R

About the Author; About the Technical Reviewer; Introduction; Chapter 1: Design of Experiments; Forming the Study Question; Forming Hypotheses; Information Required; Power and Sample Size; Calculation Information; Risk (Confidence and Power Levels); Continuous Data; Binary Data; Conducting the Calculations; Defining the Scope of the Study; Applicability of Results; Assumptions; Experimental Design; Variables; Interactions; Confounding; Designed Experiments; Physical Experiments; Factorial and Optimal Designs; Adaptive Designs; Other Designs; Computer Experiments; Surveys; SummaryChapter 2: Data CollectionCollecting the Data; Objective Data; Variation; Repeats; Precision; Subjective Data; Participants; Instructions; Repeats; Observers; Instructions; Authority; Variation; Formatting; Summary; Chapter 3: Exploratory Data Analysis; Data Types; Quantitative Data; Continuous Data; Discrete Data; Qualitative Data; Binary Data; Nominal Data; Ordinal Data; Viewing the Data; Bar Charts; Dot Plots; Parallel Lines Plots; Histograms; Scatter Plots; Line Graphs; Box Plots; Likert Plots; Trellis Graphs; Outliers; Distribution; Tests; Continuous Data; Discrete Data; SummaryChapter 4: Descriptive StatisticsContinuous Data; Shape; Skewness; Kurtosis; Transformations; Location; Mode; Median; Mean; Weighted Mean; Spread; Standard Deviation and Variance; Range; Quantiles and Percentiles; IQR and SIQR; MAD; CV; Discrete Data; Bivariate Data; Contingency Tables; Correlation and Covariance; Summary; Chapter 5: Measuring Uncertainty; Confidence Intervals; Continuous Data; Binary Data; Tolerance Intervals; Continuous Data; Binary Data; Prediction Intervals; Summary; Chapter 6: Hypothesis Testing; Hypothesis Test Components; Hypotheses; Sides or Tails; P-valuesSignificant DifferencesPractical Differences; Plots; Interpretation; Hypothesis Tests; Binary Data; One-Sample Binary Data; Two-Sample Binary Data; Paired Binary Data; Continuous Data; One-Sample Normally Distributed Data; One-Sample Non-Normally Distributed Data; Two-Sample Normally Distributed Data; Two-Sample Non-Normally Distributed Data; Paired Normally Distributed Data; Paired Non-Normally Distributed Data; Summary; Chapter 7: Statistical Modeling; Statistical Model Components; Model Assumptions; Model Structure; Model Process; Model Output; Statistical Models; Simple ModelsLinear ModelANOVA; Generalized Linear Model; Gaussian GLM; Poisson GLM; Negative Binomial GLM; Binomial GLM; Bias-Reduction Binomial-Response RGLM; Zero-Inflated Models; Ordinal Logistic Regression; Linear Mixed-Effects Models; Summary; Chapter 8: Multivariate Analysis; Multivariate Analysis of Variance; Principal Component Analysis; Q Methodology; Summary; Chapter 9: Graphs; Common Plotting Mistakes; 3D and Pie Charts; Plotting Averages; Multiple Plots; Plotting Ordinal Data; Open Text Responses; Unnecessary Plots; Display; Graph Aesthetics in R; Graphs in R; Bar Chart; Tile Plot