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Mathematical statistics -- Data processing
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The concept ** Mathematical statistics -- Data processing** represents the subject, aboutness, idea or notion of resources found in **University of Missouri Libraries**.

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Mathematical statistics -- Data processing
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**Mathematical statistics -- Data processing**represents the subject, aboutness, idea or notion of resources found in**University of Missouri Libraries**.- Label
- Mathematical statistics -- Data processing

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- A course in computational probability and statistics
- A gentle introduction to statistics using SAS studio
- A guide to TI graphing calculators
- A handbook of statistical analyses using S-PLUS
- A handbook of statistical analyses using SAS
- A handbook of statistical analyses using SAS
- A handbook of statistical analyses using Stata
- A handbook of statistical analyses using Stata
- A recipe for success using SAS University Edition : how to plan your first analytics project
- A simplified guide to using statistical techniques with computer applications
- A step-by-step approach to using the SAS system for univariate and multivariate statistics
- APL-STAT : do-it-yourself guide to computational statistics using APL
- Advanced R statistical programming and data models : analysis, machine learning, and visualization
- Advances in stochastic simulation methods
- An introduction to SASÂ® university edition
- An introduction to machine learning models in production : how to transition from one-off models to reproducible pipelines
- Analyzing and interpreting continuous data using JMP : a step-by-step guide
- Analyzing compositional data with R
- Applications, basics, and computing of exploratory data analysis
- Applied and computational statistics : a first course
- Applied statistics : a handbook of Genstat analyses
- Applied statistics : using SPSS, STATISTICA, and MATLAB
- Applied statistics : using SPSS, Statistica, MATLAB, and R
- Applied statistics : using SPSS, Statistica, MATLAB, and R
- Applied statistics and the SAS programming language
- Applied statistics and the SAS programming language
- Applied statistics and the SAS programming language
- Base SAS 9.4 procedures guide
- Base SAS 9.4 procedures guide : statistical procedures
- Basic statistical computing
- Building better models with JMP Pro
- C/C++ mathematical algorithms for scientists & engineers
- Carpenter's Complete Guide to the SAS REPORT Procedure
- Categorical data analysis using SAS
- Categorical data analysis using the SAS system
- Clinical graphs using SAS
- Combining and modifying SAS data sets : examples
- Complex data modeling and computationally intensive statistical methods
- Complex models and computational methods in statistics
- Complex surveys : a guide to analysis using R
- Computational methods for data analysis
- Computational methods for parsimonious data fitting
- Computational statistics
- Computational statistics
- Computational statistics : an introduction to R
- Computational statistics handbook with MATLAB
- Computer intensive methods in statistics
- Core concepts in data analysis : summarization, correlation and visualization
- Data analysis in management with SPSS software
- Data analysis in management with SPSS software
- Data analysis of asymmetric structures : advanced approaches in computational statistics
- Data analysis using SAS Enterprise guide
- Data analysis using Stata
- Data analysis using stata
- Data analysis with R : a comprehensive guide to manipulating, analyzing, and visualizing data in R
- Data analytics and machine learning fundamentals : LiveLessons
- Data driven statistical methods
- Data manipulation With R
- Data quality for analytics using SAS
- Data science with Python and R
- Deep learning mit R und Keras : Das Praxis-Handbuch : von Entwicklern von Keras und RStudio
- Deep learning with R
- Design of experiments guide : JMP 11
- Discovering JMP 10
- Discovering JMP 11
- Discovering JMP 13
- Elementary computer-assisted statistics
- Elementary statistics using SAS
- Elements of simulation
- Essential statistics using SAS university edition
- Excel 2007 for educational and psychological statistics : a guide to solving practical problems
- Exercises and projects for the Little SAS book, fifth edition
- Exercises and projects for the little SAS book
- Exploratory Data Analysis with R
- Exploratory and multivariate data analysis
- Exploratory factor analysis with SAS
- Exploratory factor analysis with SAS
- Extending the S system
- Foundations of statistical analyses and applications with SAS
- Fundamentals of predictive analytics with JMP
- Fundamentals of predictive analytics with JMP
- Genstat 5 release 3 reference manual
- Getting started with R for data science
- Getting started with the Graph Template Language in SAS : examples, tips, and techniques for creating custom graphs
- Guide to SAS/DB2
- Guide to intelligent data analysis : how to intelligently make sense of real data
- Handbook of statistical analysis using stata
- Hands-on data science with R : techniques to perform data manipulation and mining to build smart analytical models using R
- Hierarchical modelling for the environmental sciences : statistical methods and applications
- How SAS works : a comprehensive introduction to the SAS system
- IBM PC statistics--BASIC programs and applications
- Interactive data analysis : a practical primer
- Intermediate statistical methods and applications : a computer package approach
- Internet-scale pattern recognition : new techniques for voluminous data sets and data clouds
- Introducing Monte Carlo methods with R
- Introducing Monte Carlo methods with R
- Introduction to SAS University edition
- Introduction to scientific computing
- Introduction to statistical computer packages
- Introduction to the use of computer packages for statistical analyses
- Introductory statistics : a microcomputer approach
- JMP 10 basic analysis and graphing
- JMP 10 design of experiments guide
- JMP 10 modeling and multivariate methods
- JMP 10 scripting guide
- JMP 11 JSL syntax reference
- JMP 11 basic analysis
- JMP 11 consumer research
- JMP 11 design of experiments guide
- JMP 11 essential graphing
- JMP 11 profilers
- JMP 11 scripting guide
- JMP 11 specialized models
- JMP 11 version JSL syntax reference
- JMP 12 consumer research
- JMP 13 basic analysis
- JMP 13 consumer research
- JMP 13 design of experiments guide
- JMP 13 essential graphing
- JMP : Version 12 : basic analysis
- JMP : version 12 : JSL syntax reference
- JMP : version 12 : design of experiments guide
- JMP : version 12 : discovering JMP
- JMP : version 12 : essential graphing
- JMP : version 12 : multivariate methods
- JMP : version 12 : reliability and survival methods
- JMP : version 12 : scripting guide
- JMP : version 12 : specialized models
- JMP essentials : an illustrated step-by-step guide for new users
- JMP for basic univariate and multivariate statistics : methods for researchers and social scientists
- JMP start statistics : a guide to statistics and data analysis using JMP
- JMP start statistics : a guide to statistics and data analysis using JMP
- JMP start statistics : a guide to statistics and data analysis using JMP and JMP IN software
- JMP start statistics : a guide to statistics and data analysis using JMP, fifth edition
- JMP version 11 : Using JMP
- JMP version 11 fitting linear models
- JMP version 11 multivariate methods
- JMP version 11 quality and process methods
- JMP version 11 reliability and survival methods
- JMP version 12 : consumer research
- JMP version 12 : profilers
- JMP version 12 : quality and process methods
- JMP version 12 : using JMP
- JMP version 12 fitting linear models
- JMP version 13 JSL syntax reference
- JMP version 13 consumer research
- JMP version 13 design of experiments guide
- JMP version 13 fitting linear models
- JMP version 13 fitting linear models
- JMP version 13 multivariate methods
- JMP version 13 multivariate methods
- JMP version 13 predictive and specialized modeling
- JMP version 13 predictive and specialized modeling
- JMP version 13 profilers
- JMP version 13 profilers
- JMP version 13 quality and process methods
- JMP version 13 reliability and survival methods
- JMP version 13 scripting guide
- JSL companion : applications of the JMP scripting language
- JSL companion : applications of the JMP scripting language
- Jmp 10 quality and reliability methods
- Jump into JMP Scripting
- Just enough SAS : a quick-start guide to SAS for engineers
- Learning Python data analysis
- Lisp-Stat : an object-oriented environment for statistical computing and dynamic graphics
- Longitudinal data analysis for the behavioral sciences using R
- MACSYMA for statisticians
- Machine Learning with R Cookbook - Second Edition : Analyze data and build predictive models
- Machine learning avec R
- Machine learning in production : developing and optimizing data science workflows and applications
- Machine learning with R cookbook : analyze data and build predictive models
- Machine learning with R cookbook : explore over 110 recipes to analyze data and build predictive models with the simple and easy-to-use R code
- Mastering data analysis with R
- Mastering the SAS system
- Mathematical statistics with Mathematica
- Mathematical statistics with applications in R
- Mathematical statistics with resampling and R
- Mathematics for data science and machine learning using R
- Median selection on the reconfigurable mesh
- Metadata management in statistical information processing : a unified framework for metadata-based processing of statistical data aggregates
- Minitab handbook
- Modern applied statistics with S
- Modern applied statistics with S-Plus
- Multidimensional clustering algorithms
- Multilevel and longitudinal modeling using Stata
- Multiple imputation of missing data using SAS
- ODS techniques : tips for enhancing your SAS output
- Omnitab II
- PROC TEMPLATE made easy : a guide for SAS users
- Parallel R
- Practical Data Science with R, Second Edition
- Practical data analysis with JMP
- Practical data analysis with JMP
- Practical data science cookbook : practical recipes on data pre-processing, analysis and visualization using R and Python
- Practical data science with R
- Practical data science with R : video edition
- Preparing data for analysis with JMP
- Probability and statistics for computer science
- Probability and statistics for data science : math + R + data
- Probability and statistics with reliability, queuing, and computer science applications
- Probability and statistics with reliability, queuing, and computer science applications
- Probability and statistics with reliability, queuing, and computer science applications
- R for data science cookbook : over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques
- R in a nutshell
- R in a nutshell
- R in a nutshell
- R machine learning solution
- R through Excel : a spreadsheet interface for statistics, data analysis, and graphics
- RStudio for R statistical computing cookbook : over 50 practical and useful recipes to help you perform data analysis with R by unleashing every native RStudio feature
- S : an interactive environment for data analysis and graphics
- SAS 9.4 ODS graphics procedures guide
- SAS 9.4 ODS graphics procedures guide
- SAS 9.4 Output Delivery System, 4th Edition
- SAS 9.4 SQL procedure : user's guide
- SAS 9.4 SQL procedure : user's guide
- SAS 9.4 graph template language : user's guide
- SAS 9.4 language reference : concepts
- SAS 9.4 language reference : concepts
- SAS 9.4 language reference concepts
- SAS 9.4 output delivery system : user's guide
- SAS certification prep guide : base programming for SAS 9
- SAS essentials : mastering SAS for data analytics
- SAS essentials : mastering SAS for data analytics
- SAS for Mixed Models
- SAS for data analysis : intermediate statistical methods
- SAS for dummies
- SAS for mixed models
- SAS for mixed models
- SAS functions by example
- SAS language : reference : version 6
- SAS procedures guide : version 6
- SAS programming for Enterprise guide users
- SAS statistics by example
- SAS system for elementary statistical analysis
- SAS user's guide : basics
- SAS/ETS user's guide : version 6
- SAS/STAT user's guide : version 6
- SPSS for starters, Part 2
- Sharpening your advanced SAS skills
- Statistical analysis and data display : an intermediate course with examples in S-plus, R, and SAS
- Statistical analysis with R
- Statistical computation
- Statistical computation; proceedings
- Statistical computing
- Statistical computing with R
- Statistical models in S
- Statistics and data analysis for microarrays using R and Bioconductor
- Statistics and measurement concepts with OpenStat
- Statistics for Machine Learning
- Statistics with Maple
- Statistics with Stata : updated for version 12
- Statistics with Stata : updated for version 9
- Step-by-step programming with Base SAS 9.4
- Step-by-step programming with Base SAS 9.4
- Systat 7.0 command reference
- Teaching elementary statistics with JMP
- Teaching elementary statistics with JMP : the power to know
- The Little SAS Book, 6th Edition
- The Little SAS book for Enterprise Guide 4.2
- The R book
- The R book
- The R book
- The R software : fundamentals of programming and statistical analysis
- The SAS workbook
- The basics of S and S-Plus
- The basics of S-PLUS
- The basics of S-Plus
- The basics of S-Plus
- The little SAS book : a primer
- The little SAS book : a primer
- The little SAS book : a primer
- The little SAS book : a primer
- The little SAS book : a primer : a programming approach
- The little SAS book for Enterprise Guide 4.1
- The numerati
- The statistical analysis of categorical data
- Two-way analysis of variance : statistical tests and graphics using R
- Understanding statistical concepts using S-plus
- Understanding statistics using R
- User's manual for IDA
- Using JMP 10
- Using JMP 11
- Using JMP 12
- Using JMP 12 student edition for Windows and Macintosh : the user's guide to statistics with JMP 12 student edition
- Using JMP 13
- Using R and RStudio for data management, statistical analysis, and graphics
- XploRe : an interactive statistical computing environment

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