R Companion for Sampling

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A01=Sharon L. Lohr
A01=Yan Lu
advanced survey data analysis in R
ANOVA Table
Author_Sharon L. Lohr
Author_Yan Lu
Bootstrap
BRR
Bubble Plot
Category=PBT
Chi-Square Tests
Cluster Sample
Cluster Sampling
Combined Ratio Estimator
complex survey analysis
Contingency Tables
Data Set
educational research statistics
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Finite Population Correction
Full Sample Weight
Inclusion Probabilities
Item Nonresponse
Jackknife
Jackknife Weights
Joint Inclusion Probabilities
Loglinear Models
Missing Data
Missing Values
Model Based Design
Model-Based Design and Analysis
NHANES Data
Odds Ratios
Poststratification
Poststratification and Raking
probability sampling techniques
public health survey data
Random Sampling
Ratio and Regression
Regression Model
Replicate Weight
Scatterplots
statistical inference in R
Stratified Multistage Samples. Univariate Plots
Stratified Random Sample
Stratified Sampling
Subset Function
survey methodology
Survey Package
Survey Weights
Unequal Probabilities
Unequal Probability Samples
Variance Estimation Method

Product details

  • ISBN 9781032135946
  • Weight: 500g
  • Dimensions: 178 x 254mm
  • Publication Date: 25 Nov 2021
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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The R Companion for Sampling: Design and Analysis, designed to be read alongside Sampling: Design and Analysis, Third Edition by Sharon L. Lohr (SDA; 2022, CRC Press), shows how to use functions in base R and contributed packages to perform calculations for the examples in SDA.

No prior experience with R is needed. Chapter 1 tells you how to obtain R and RStudio, introduces basic features of the R statistical software environment, and helps you get started with analyzing data.

Each subsequent chapter provides step-by-step guidance for working through the data examples in the corresponding chapter of SDA, with code, output, and interpretation. Tips and warnings help you develop good programming practices and avoid common survey data analysis errors.

R features and functions are introduced as they are needed so you can see how each type of sample is selected and analyzed. Each chapter builds on the knowledge developed earlier for simpler designs; after finishing the book, you will know how to use R to select and analyze almost any type of probability sample.

All R code and data sets used in this book are available online to help you develop your skills analyzing survey data from social and public opinion research, public health, crime, education, business, agriculture, and ecology.

Yan Lu is Associate Professor of Statistics at the University of New Mexico. Her research interests include survey sampling, mixed models, nonparametric regression, and data mining. Recent publications develop new statistical methods for combining data from multiple surveys, selecting probability samples from massive data streams, and applying nonparametric regression to survey data.

Sharon L. Lohr, the author of Measuring Crime: Behind the Statistics, has published widely about survey sampling and statistical methods for education, public policy, law, and crime. She is a Fellow of the American Statistical Association and an elected member of the International Statistical Institute, and has received the Gertrude M. Cox, Morris Hansen, and Deming Awards. Formerly Dean’s Distinguished Professor of Statistics at Arizona State University and a Vice President at Westat, she is now a statistical consultant and writer.

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