دانلود کتاب Statistical Application Development with R and Python - Second Edition

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Cover
Copyright
Credits
About the Author
Acknowledgment
About the Reviewers
www.PacktPub.com
Customer Feedback
Table of Contents
Preface
Chapter 1: Data Characteristics
Questionnaire and its components
Understanding the data characteristics in an R environment
Experiments with uncertainty in computer science
Installing and setting up R
Using R packages
RSADBE – the books R package
Python installation and setup
Using pip for packages
IDEs for R and Python
The companion code bundle
Discrete distributions
Discrete uniform distribution
Binomial distribution
Hypergeometric distribution
Negative binomial distribution
Poisson distribution
Continuous distributions
Uniform distribution
Exponential distribution
Normal distribution
Summary
Chapter 2: Import/Export Data
Packages and settings – R and Python
Understanding data.frame and other formats
Constants, vectors, and matrices
Time for action – understanding constants, vectors, and basic arithmetic
What just happened?
Doing it in Python
Time for action – matrix computations
What just happened?
Doing it in Python
The list object
Time for action – creating a list object
What just happened?
The data.frame object
Time for action – creating a data.frame object
What just happened?
Have a go hero
The table object
Time for action – creating the Titanic dataset as a table object
What just happened?
Have a go hero
Using utils and the foreign packages
Time for action – importing data from external files
What just happened?
Doing it in Python
Importing data from MySQL
Doing it in Python
Exporting data/graphs
Exporting R objects
Exporting graphs
Time for action – exporting a graph
What just happened?
Managing R sessions
Time for action – session management
What just happened?
Doing it in Python
Pop quiz
Summary
Chapter 3: Data Visualization
Packages and settings – R and Python
Visualization techniques for categorical data
Bar chart
Going through the built-in examples of R
Time for action – bar charts in R
What just happened?
Doing it in Python
Have a go hero
Dot chart
Time for action – dot charts in R
What just happened?
Doing it in Python
Spine and mosaic plots
Time for action – spine plot for the shift and operator data
What just happened?
Time for action – mosaic plot for the Titanic dataset
What just happened?
Pie chart and the fourfold plot
Visualization techniques for continuous variable data
Boxplot
Time for action – using the boxplot
What just happened?
Doing it in Python
Histogram
Time for action – understanding the effectiveness of histograms
What just happened?
Doing it in Python
Have a go hero
Scatter plot
Time for action – plot and pairs R functions
What just happened?
Doing it in Python
Have a go hero
Pareto chart
A brief peek at ggplot2
Time for action – qplot
What just happened?
Time for action – ggplot
What just happened?
Pop quiz
Summary
Chapter 4: Exploratory Analysis
Packages and settings – R and Python
Essential summary statistics
Percentiles, quantiles, and median
Hinges
Interquartile range
Time for action – the essential summary statistics for The Wall dataset
What just happened?
Techniques for exploratory analysis
The stem-and-leaf plot
Time for action – the stem function in play
What just happened?
Letter values
Data re-expression
Have a go hero
Bagplot – a bivariate boxplot
Time for action – the bagplot display for multivariate datasets
What just happened?
Resistant line
Time for action – resistant line as a first regression model
What just happened?
Smoothing data
Time for action – smoothening the cow temperature data
What just happened?
Median polish
Time for action – the median polish algorithm
What just happened?
Have a go hero
Summary
Chapter 5: Statistical Inference
Packages and settings – R and Python
Maximum likelihood estimator
Visualizing the likelihood function
Time for action – visualizing the likelihood function
What just happened?
Doing it in Python
Finding the maximum likelihood estimator
Using the fitdistr function
Time for action – finding the MLE using mle and fitdistr functions
What just happened?
Confidence intervals
Time for action – confidence intervals
What just happened?
Doing it in Python
Hypothesis testing
Binomial test
Time for action – testing probability of success
What just happened?
Tests of proportions and the chi-square test
Time for action – testing proportions
What just happened?
Tests based on normal distribution – one sample
Time for action – testing one-sample hypotheses
What just happened?
Have a go hero
Tests based on normal distribution – two sample
Time for action – testing two-sample hypotheses
What just happened?
Have a go hero
Doing it in Python
Summary
Chapter 6: Linear Regression Analysis
Packages and settings - R and Python
The essence of regression
The simple linear regression model
What happens to the arbitrary choice of parameters?
Time for action - the arbitrary choice of parameters
What just happened?
Building a simple linear regression model
Time for action - building a simple linear regression model
What just happened?
Have a go hero
ANOVA and the confidence intervals
Time for action - ANOVA and the confidence intervals
What just happened?
Model validation
Time for action - residual plots for model validation
What just happened?
Doing it in Python
Have a go hero
Multiple linear regression model
Averaging k simple linear regression models or a multiple linear regression model
Time for action - averaging k simple linear regression models
What just happened?
Building a multiple linear regression model
Time for action - building a multiple linear regression model
What just happened?
The ANOVA and confidence intervals for the multiple linear regression model
Time for action - the ANOVA and confidence intervals for the multiple linear regression model
What just happened?
Have a go hero
Useful residual plots
Time for action - residual plots for the multiple linear regression model
What just happened?
Regression diagnostics
Leverage points
Influential points
DFFITS and DFBETAS
The multicollinearity problem
Time for action - addressing the multicollinearity problem for the gasoline data
What just happened?
Doing it in Python
Model selection
Stepwise procedures
The backward elimination
The forward selection
The stepwise regression
Criterion-based procedures
Time for action - model selection using the backward, forward, and AIC criteria
What just happened?
Have a go hero
Summary
Chapter 7: Logistic Regression Model
Packages and settings – R and Python
The binary regression problem
Time for action – limitation of linear regression model
What just happened?
Probit regression model
Time for action – understanding the constants
What just happened?
Doing it in Python
Logistic regression model
Time for action – fitting the logistic regression model
What just happened?
Doing it in Python
Hosmer-Lemeshow goodness-of-fit test statistic
Time for action – Hosmer-Lemeshow goodness-of-fit statistic
What just happened?
Model validation and diagnostics
Residual plots for the GLM
Time for action – residual plots for logistic regression model
What just happened?
Doing it in Python
Have a go hero
Influence and leverage for the GLM
Time for action – diagnostics for the logistic regression
What just happened?
Have a go hero
Receiving operator curves
Time for action – ROC construction
What just happened?
Doing it in Python
Logistic regression for the German credit screening dataset
Time for action – logistic regression for the German credit dataset
What just happened?
Doing it in Python
Have a go hero
Summary
Chapter 8: Regression Models with Regularization
Packages and settings – R and Python
The overfitting problem
Time for action – understanding overfitting
What just happened?
Doing it in Python
Have a go hero
Regression spline
Basis functions
Piecewise linear regression model
Time for action – fitting piecewise linear regression models
What just happened?
Natural cubic splines and the general B-splines
Time for action – fitting the spline regression models
What just happened?
Ridge regression for linear models
Protecting against overfitting
Time for action – ridge regression for the linear regression model
What just happened?
Doing it in Python
Ridge regression for logistic regression models
Time for action – ridge regression for the logistic regression model
What just happened?
Another look at model assessment
Time for action – selecting  iteratively and other topics
What just happened?
Pop quiz
Summary
Chapter 9: Classification and Regression Trees
Packages and settings – R and Python
Understanding recursive partitions
Time for action – partitioning the display plot
What just happened?
Splitting the data
The first tree
Time for action – building our first tree
What just happened?
Constructing a regression tree
Time for action – the construction of a regression tree
What just happened?
Constructing a classification tree
Time for action – the construction of a classification tree
What just happened?
Doing it in Python
Classification tree for the German credit data
Time for action – the construction of a classification tree
What just happened?
Doing it in Python
Have a go hero
Pruning and other finer aspects of a tree
Time for action – pruning a classification tree
What just happened?
Pop quiz
Summary
Chapter 10: CART and Beyond
Packages and settings – R and Python
Improving the CART
Time for action – cross-validation predictions
What just happened?
Understanding bagging
The bootstrap
Time for action – understanding the bootstrap technique
What just happened?
How the bagging algorithm works
Time for action – the bagging algorithm
What just happened?
Doing it in Python
Random forests
Time for action – random forests for the German credit data
What just happened?
Doing it in Python
The consolidation
Time for action – random forests for the low birth weight data
What just happened?
Summary
Index