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Springer texts in statistics
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The series ** Springer texts in statistics** represents a set of related resources, especially of a specified kind, found in **University of Missouri Libraries**.

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Springer texts in statistics
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**Springer texts in statistics**represents a set of related resources, especially of a specified kind, found in**University of Missouri Libraries**.- Label
- Springer texts in statistics

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- Springer texts in statistics, 103
- Springer texts in statistics, 75
- Springer texts in statistics, 95
- Springer texts in statistics, v. 75
- A first course in Bayesian statistical methods
- A modern approach to regression with R
- A modern introduction to probability and statistics : understanding why and how
- Advanced linear modeling : multivariate, time series, and spatial data; nonparametric regression and response surface maximization
- All of nonparametric statistics
- All of nonparametric statistics
- All of statistics : a concise course in statistical inference
- An R AND S-PLUS companion to multivariate analysis
- An R and S-PLUS companion to multivariate analysis
- An intermediate course in probability
- An intermediate course in probability
- An introduction to Bayesian analysis : theory and methods
- An introduction to Bayesian analysis : theory and methods
- An introduction to probability and stochastic processes
- An introduction to statistical learning : with applications in R
- Analysis of variance in experimental designs
- Analyzing categorical data
- Applied Bayesian statistics : with R and OpenBUGS examples
- Applied Bayesian statistics : with R and OpenBUGS examples
- Applied mathematical demography
- Applied multivariate analysis
- Applied multivariate data analysis
- Applied probability
- Applied probability
- Applied regression analysis : a research tool
- Applying generalized linear models
- Asymptotic theory of statistics and probability
- Asymptotic theory of statistics and probability
- Basic principles of structural equation modeling : an introduction to LISREL and EQS
- Basics of modern mathematical statistics : exercises and solutions
- Bayesian core : a practical approach to computational Bayesian statistics
- Bayesian core : a practical approach to computational Bayesian statistics
- Bayesian essentials with R
- Elements of large-sample theory
- Elements of statistics for the life and social sciences
- Essential Statistical Inference : Theory and Methods
- Essentials of stochastic processes
- Essentials of stochastic processes
- Essentials of stochastic processes
- Fundamentals of mathematical statistics
- Fundamentals of probability : a first course
- Fundamentals of probability : a first course
- Graphical exploratory data analysis
- Introduction to modeling and analysis of stochastic systems
- Introduction to statistical inference
- Introduction to time series and forecasting
- Large sample techniques for statistics
- Linear mixed-effects models using R : a step-by-step approach
- Linear models for multivariate, time series, and spatial data
- Log-linear models
- Log-linear models and logistic regression
- Mathematical statistics
- Mathematical statistics with Mathematica
- Matrix algebra : theory, computations, and applications in statistics
- Measure theory and probability theory
- Measure theory and probability theory
- Modeling longitudinal data
- Modeling longitudinal data
- Modern mathematical statistics with applications
- Modern multivariate statistical techniques : regression, classification, and manifold learning
- Monte Carlo statistical methods
- Monte Carlo statistical methods
- Optimization
- Plane answers to complex questions : the theory of linear models
- Plane answers to complex questions : the theory of linear models
- Plane answers to complex questions : the theory of linear models
- Prescriptions for working statisticians
- Probability
- Probability
- Probability : a Graduate Course
- Probability : a graduate course
- Probability and statistical inference
- Probability and statistics : theory and applications
- Probability for statisticians
- Probability for statisticians
- Probability for statistics and machine learning : fundamentals and advanced topics
- Probability for statistics and machine learning : fundamentals and advanced topics
- Probability theory : independence, interchangeability, martingales
- Probability via expectation
- Regression analysis : theory, methods and applications
- Statistical analysis and data display : an intermediate course with examples in S-plus, R, and SAS
- Statistical analysis of designed experiments
- Statistical analysis of financial data in R
- Statistical design
- Statistical methods : the geometric approach
- Statistical models and methods for financial markets
- Statistical tables and formulae
- Statistics and data analysis for financial engineering
- Statistics for bioengineering sciences : with MATLAB and WinBUGS support
- Statistics for lawyers
- Statistics in scientific investigation : its basis, application, and interpretation
- Studying human populations : an advanced course in statistics
- Testing statistical hypotheses
- Testing statistical hypotheses
- Testing statistical hypotheses
- The Bayesian choice : a decision-theoretic motivation
- The Bayesian choice : from decision-theoretic foundations to computational implementation
- The Bayesian choice : from decision-theoretic foundations to computational implementation
- The statistical analysis of discrete data
- Theoretical statistics : topics for a core course
- Theory of multivariate statistics
- Theory of point estimation
- Time series analysis : with applications in R
- Time series analysis and its applications
- Time series analysis and its applications : with R examples
- Time series analysis and its applications : with R examples
- Time series analysis and its applications : with R examples

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