Using R for Introductory Statistics 2nd Edition by John Verzani – Ebook PDF Instant Download/Delivery: 1466590734, 978-1466590731
Full download Using R for Introductory Statistics 2nd edition after payment
Product details:
ISBN 10: 1466590734
ISBN 13: 978-1466590731
Author: John Verzani
The second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version.
Using R for Introductory Statistics 2nd Table of contents:
1 Getting started
1.1 What is data?
1.2 Getting started with R
Installing R
Installing RStudio
R’s command line
Variables
Functions
The workspace
External packages
Data sets
Problems
2 Univariate data
2.1 Data vectors
Structured data
Indexing
Data types
Numeric data types
Categorical data types
Date and time types
Logical data
Problems
2.2 Functions
Problems
2.3 Numeric summaries
Center
The sample mean
The sample median
Measures of position
Other measures of center
Spread
The variance and standard deviation
The IQR
Shape
Viewing the shape of a data set
Problems
2.4 Categorical data
Problems
3 Bivariate data
3.1 Independent samples
Problems
3.2 Data manipulation basics
Lists
Data frames
Model formulas
Problems
3.3 Paired data
Correlation
Trends
Transformations
Alternative trend lines
Problems
3.4 Bivariate categorical data
Tables
Two-way tables from summarized data
Two-way tables from unsummarized data
Marginal distributions of two-way tables
Conditional distributions of two-way tables
The xtabs function
Graphical summaries of two-way contingency tables
Mosaic plots
Measures of association for categorical data
Problems
4 Multivariate data
4.1 Data structures in R
Problems
4.2 Working with data frames
Problems
4.3 Applying a function over a collection
Map
Filter
Reduce
Problems
4.4 Using external data
Spreadsheet data
Web-based data sets
5 Multivariate graphics
5.1 Base graphics
Problems
5.2 Lattice graphics
Problems
5.3 The ggplot2 package
Geoms
Grouping
Statistical transformations
Faceting
Problems
6 Populations
6.1 Populations
Discrete random variables
Using sample to generate random values
The mean and standard deviation
Continuous random variables
The p.d.f. and c.d.f.
The mean and standard deviation
Quantiles
Sampling from a population
Random samples generated by sample
Sampling distributions
Problems
6.2 Families of distributions
The d, p, q, and r functions
Binomial, normal, and some other named distributions
Bernoulli random variables
Binomial random variables
Normal random variables
Popular distributions to describe populations
Uniform distribution
Exponential distribution
Lognormal distribution
Sampling distributions
Problems
6.3 The central limit theorem
Normal parent population
Nonnormal parent population
Problems
7 Statistical inference
7.1 Simulation
Repeating a simulation easily
Problems
7.2 Significance tests
7.3 Estimation, confidence intervals
The basic bootstrap
7.4 Bayesian analysis
8 Confidence intervals
8.1 Confidence intervals for a population proportion, p
Problems
8.2 Confidence intervals for the population mean
One-sided confidence intervals
Problems
8.3 Other confidence intervals
Confidence interval for σ2
Problems
8.4 Confidence intervals for differences
Difference of proportions
Difference of means
Matched samples
Problems
8.5 Confidence intervals for the median
Confidence intervals based on the binomial distribution
Confidence intervals based on signed-rank statistic
Confidence intervals based on the rank-sum statistic
Problems
9 Significance tests
9.1 Significance test for a population proportion
Using prop.test to compute p-values
Problems
9.2 Significance test for the mean (t-tests)
Power
Problems
9.3 Significance tests and confidence intervals
9.4 Significance tests for the median
The sign test
The signed-rank test
Problems
9.5 Two-sample tests of proportion
Problems
9.6 Two-sample tests of center
Two-sample tests of center with normal populations
Matched samples
The Wilcoxon rank-sum test for equality of center
Problems
10 Goodness of fit
10.1 The chi-squared goodness-of-fit test
The multinomial distribution
Pearson’s χ2-statistic
Partially specified null hypotheses
Problems
10.2 The chi-squared test of independence
The chi-squared test of homogeneity
Problems
10.3 Goodness-of-fit tests for continuous distributions
Kolmogorov-Smirnov test
The Shapiro-Wilk test for normality
Finding parameter values using fitdistr
Problems
11 Linear regression
11.1 The simple linear regression model
Estimating the parameters in simple linear regression
Using lm to find the estimates
Extractor functions for lm
Problems
11.2 Statistical inference for simple linear regression
Statistical inferences
Marginal t-tests
The F-test
R2—the coefficient of determination
Using lm to find values for a regression model
Confidence intervals
Standard error
Significance tests
Finding Q2, R2
F-test for β1 = 0
Testing the model assumptions
Assessing the linear model for the mean
Assessing the residuals
Influential points
Prediction intervals
Confidence intervals for μy|x
Problems
11.3 Multiple linear regression
Types of models
Fitting the multiple regression model using lm
Using update with model formulas
Interpreting the regression parameters
Statistical inferences
Model selection
Partial F-test
The Akaike information criterion
Problems
12 Analysis of variance
12.1 One-way ANOVA
Using R’s model formulas to specify ANOVA models
Using oneway.test to perform ANOVA
Using aov for ANOVA
The nonparametric Kruskal–Wallis test
Problems
12.2 Using lm for ANOVA
Treatment coding for analysis of variance
Comparing multiple differences
Problems
12.3 Ancova
Problems
12.4 Two-way ANOVA
Interaction plots
Fitting a two-way ANOVA
Blocking variables
Problems
13 Extensions of the linear model
13.1 Logistic regression
Generalized linear models
Fitting the model usmodelsy
13.2 Nonlinear modelsy
People also search for Using R for Introductory Statistics 2nd:
simpler using r for introductory statistics
verzani using r for introductory statistics
using r for introductory statistics pdf
john verzani simpler using r for introductory statistics
using r for introductory statistics 2nd edition pdf