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Better business decisions from data : statistical analysis for professional success /
This book discusses what statistics are really saying or not saying. It shows how to use statistical data to improve small, every-day management judgments as well as major business decisions with potentially serious consequences. This book lays a foundation for understanding the importance and value...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berkeley, CA :
Apress,
2014.
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| Colección: | Expert's voice in data analysis.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Introduction; Part I:Uncertainties; Chapter 1: The Scarcity of Certainty; Chapter 2: Sources of Uncertainty; Statistical Data; Processing the Data; Chapter 3: Probability; Probability Defined; Combining Probabilities; Conditional Probability; Part II:Data; Chapter 4: Sampling; Problems with Sampling; Repeated Measurements; Simple Random Sampling; Systematic Sampling; Stratified Random Sampling; Cluster Sampling; Quota Sampling; Sequential Sampling; Databases; Resampling Methods; Data Sequences; Chapter 5: The Raw Data; Descriptive or Numerical; Format of Numbers; Rounding.
- PercentagesSimple Index Numbers; Part III:SamplesThe; Chapter 6: Descriptive Data; Diagrammatic Representation; Proportion; Chapter 7: Numerical Data; Diagrammatic Representation; Normally Distributed Data; Distribution Type; Averages; Spread of Data; Grouped Data; Pooling and Weighting; Estimated Population Properties; Confidence Intervals; Part IV:Comparisons; Chapter 8: Levels of Significance; Chapter 9: General Procedure for Comparisons; Chapter 10: Comparisons with Numerical Data; Single Value; Mean of a Sample; Difference between Variances; Difference between Means; Means of Paired Data.
- Multiple SamplesChapter 11: Comparisons with Descriptive Data; Single Proportion; Difference between Proportions; Ranks; Ranks of Paired Data; Duplicate Ranks; Chapter 12: Types of Error; Part V:Relationships; Chapter 13: Cause and Effect; Chapter 14: Relationships with Numerical Data; Linear Relationships; Nonlinear Relationships; Irregular Relationships; Chapter 15: Relationships with Descriptive Data; Nominal Data; Ordinal Data; Chapter 16: Multivariate Data; Multiple Regression; Analysis of Variance; Latin and Graeco-Latin Squares; Multidimensional Contingency Tables.
- Multivariate Analysis of VarianceConjoint Analysis; Proximity Maps; Structural Equation Modeling; Association: Some Further Methods; Part VI:Forecasts; Chapter 17: Extrapolation; Chapter 18: Forecasting from Known Distributions; Uniform Distribution; Normal Distribution; Binomial Distribution; Poisson Distribution; Exponential Distribution; Geometric Distribution; Weibull Distribution; Chapter 19: Time Series; Regression; Autocorrelation; Exponential Smoothing; Chapter 20: Control Charts; Sampling by Variable; Sampling by Attribute; Chapter 21: Reliability; Basic Principles; Reliability Data.
- DistributionsPractical Complications; Part VII:Big Data; Chapter 22: Data Mining; The Growth of Data; Data Warehouses; Future Developments; Chapter 23: Predictive Analytics; Simple Rules; Decision Trees; Association; Clustering; Neural Networks; Ensembles; Chapter 24: Getting Involved with Big Data; Applications; The Big Players; The Smaller Options; Chapter 25: Concerns with Big Data; Security; Privacy; Skills Shortage; A New Concept; Chapter 26: References and Further Reading; References; Further Reading; Index; Preface; About the Author; Acknowledgments.