Translating statistics to make decisions : a guide for the non-statistician
By Victoria Cox
- 1
- Apress,
- c2017.
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.
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; Summary.
Chapter 7: Statistical Modeling; Statistical Model Components; Model Assumptions; Model Structure; Model Process; Model Output; Statistical Models; Simple Models. Linear 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
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