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Data analysis with R : load, wrangle, and analyze your data using the world's most powerful statistical programming language /
Annotation
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2015.
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| Colección: | Community experience distilled.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Copyright; Credits; About the Author; About the Reviewer; www.PacktPub.com; Table of Contents; Preface; Chapter 1: RefresheR; Navigating the basics; Arithmetic and assignment; Logicals and characters; Flow of control; Getting help in R; Vectors; Subsetting; Vectorized functions; Advanced subsetting; Recycling; Functions; Matrices; Loading data into R; Working with packages; Exercises; Summary; Chapter 2: The Shape of Data; Univariate data; Frequency distributions; Central tendency; Spread; Populations, samples, and estimation; Probability distributions; Visualization methods; Exercises.
- The binomial distributionThe normal distribution; The three-sigma rule and using z-tables; Exercises; Summary; Chapter 5: Using Data to Reason About the World; Estimating means; The sampling distribution; Interval estimation; How did we get 1.96?; Smaller samples; Exercises; Summary; Chapter 6: Testing Hypotheses; Null Hypothesis Significance Testing; One and two-tailed tests; When things go wrong; A warning about significance; A warning about p-values; Testing the mean of one sample; Assumptions of the one sample t-test; Testing two means; Don't be fooled!
- Assumptions of the independent samples t-testTesting more than two means; Assumptions of ANOVA; Testing independence of proportions; What if my assumptions are unfounded?; Exercises; Summary; Chapter 7: Bayesian Methods; The big idea behind Bayesian analysis; Choosing a prior; Who cares about coin flips; Enter MCMC
- stage left; Using JAGS and runjags; Fitting distributions the Bayesian way; The Bayesian independent samples t-test; Exercises; Summary; Chapter 8: Predicting Continuous Variables; Linear models; Simple linear regression; Simple linear regression with a binary predictor.
- A word of warningMultiple regression; Regression with a non-binary predictor; Kitchen sink regression; The bias-variance trade-off; Cross-validation; Striking a balance; Linear regression diagnostics; Second Anscombe relationship; Third Anscombe relationship; Fourth Anscombe relationship; Advanced topics; Exercises; Summary; Chapter 9: Predicting Categorical Variables; k-Nearest Neighbors; Using k-NN in R; Confusion matrices; Limitations of k-NN; Logistic regression; Using logistic regression in R; Decision trees; Random forests; Choosing a classifier; The vertical decision boundary.