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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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- http://id.worldcat.org/fast/01012133

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- A gentle introduction to statistics using SAS studio
- A handbook of statistical analyses using R
- A recipe for success using SAS University Edition : how to plan your first analytics project
- Advanced R statistical programming and data models : analysis, machine learning, and visualization
- Advanced statistics with applications in R
- Advances in Mathematical and Statistical Modeling
- Advances in intelligent data analysis : 4th international conference, IDA 2001, Cascais, Portugal, September 13-15, 2001 : proceedings
- Advances in intelligent data analysis : reasoning about data : second international symposium, IDA-97, London, UK, August 4-6, 1997 : proceedings
- Advances in intelligent data analysis : third international symposium, IDA-99, Amsterdam, the Netherlands, August 9-11, 1999 : proceedings
- Advances in intelligent data analysis IX : 9th international symposium, IDA 2010, Tucson, AZ, USA, May 19-21, 2010 : proceedings
- Advances in intelligent data analysis V : 5th International Symposium on Intelligent Data Analysis, IDA 2003, Berlin, Germany, August 28-30, 2003 : proceedings
- Advances in intelligent data analysis VI : 6th International Symposium on Intelligent Data Analysis, IDA 2005, Madrid, Spain, September 8-10, 2005 : proceedings
- Advances in intelligent data analysis VII : 7th International Symposium on Intelligent Data Analysis, IDA 2007, Ljubljana, Slovenia, September 6-8, 2007 : proceedings
- Advances in intelligent data analysis VIII : 8th International Symposium on Intelligent Data Analysis, IDA 2009, Lyon, France, August 31 - September 2, 2009 ; proceedings
- Advances in intelligent data analysis X : 10th international symposium, IDA 2011, Porto, Portugal, October 29-31, 2011 : proceedings
- Advances in intelligent data analysis XI : 11th international symposium, IDA 2012, Helsinki, Finland, October 25-27, 2012 : proceedings
- Advances in intelligent data analysis XII : 12th International Symposium, IDA 2013, London, UK, October 17-19, 2013. Proceedings
- Advances in intelligent data analysis XIII : 13th International Symposium, IDA 2014, Leuven, Belgium, October 30-November 1, 2014. Proceedings
- 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
- Base SAS 9.4 procedures guide
- Building better models with JMP Pro
- COMPSTAT 2008 : proceedings in computational statistics : 18th Symposium held in Porto, Portugal, 2008
- Carpenter's Complete Guide to the SAS REPORT Procedure
- Categorical data analysis using SAS
- Categorical data analysis using the SAS system
- Challenges at the interface of data analysis, computer science, and optimization : proceedings of the 34th Annual Conference of the Gesellschaft fÃ¼r Klassifikation e. V., Karlsruhe, July 21-23, 2010
- Chance
- 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
- Computational statistics
- Computational statistics
- Computational statistics
- Computational statistics : an introduction to R
- Core concepts in data analysis : summarization, correlation and visualization
- Data analysis in management with SPSS software
- 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 manipulation With R
- 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 statistics using SAS
- 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
- Exploratory factor analysis with SAS
- Extending R
- Fundamentals of predictive analytics with JMP
- Fundamentals of predictive analytics with JMP
- 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 intelligent data analysis : how to intelligently make sense of real data
- Handbook of computational statistics : concepts and methods
- Hands-on data science with R : techniques to perform data manipulation and mining to build smart analytical models using R
- Intelligent data analysis
- Internet-scale pattern recognition : new techniques for voluminous data sets and data clouds
- Introducing Monte Carlo methods with R
- Introduction to SAS University edition
- 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 essential graphing
- JMP 11 profilers
- JMP 11 scripting guide
- JMP 11 specialized models
- JMP 11 version JSL syntax reference
- 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, 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
- 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
- Mathematical statistics with applications in R
- Mathematical statistics with resampling and R
- Multilevel and longitudinal modeling using Stata
- Multiple imputation of missing data using SAS
- New perspectives in statistical modeling and data analysis : proceedings of the 7th Conference of the Classification and Data Analysis Group of the Italian Statistical Society, Catania, September 9-11, 2009
- ODS techniques : tips for enhancing your SAS output
- 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 data science : math + R + data
- 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
- 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
- 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 for Mixed Models
- SAS for data analysis : intermediate statistical methods
- SAS for dummies
- SAS for mixed models
- SAS functions by example
- SAS programming for Enterprise guide users
- SAS statistics by example
- SIAM journal on scientific and statistical computing
- SSDBM 2007 : 19th International Conference on Scientific and Statistical Database Management : 9-11 July 2007, Banff, Alberta, Canada
- Scientific and statistical database management : 20th international conference, SSDBM 2008, Hong Kong, China, July 9-11, 2008 : proceedings
- Scientific and statistical database management : 21st international conference, SSDBM 2009, New Orleans, LA, USA, June 2-4, 2009 : proceedings
- Scientific and statistical database management : 23rd international conference, SSDBM 2011, Portland, OR, USA, July 20-22, 2011 : proceedings
- Sharpening your advanced SAS skills
- Statistical analysis with R
- Statistical computing with R
- Statistics and computing
- Statistics and data analysis for microarrays using R and Bioconductor
- Statistics and measurement concepts with OpenStat
- Statistics with Stata : updated for version 12
- Step-by-step programming with Base SAS 9.4
- Step-by-step programming with Base SAS 9.4
- 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 journal
- The R software : fundamentals of programming and statistical analysis
- The SAS workbook
- The basics of S-Plus
- 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
- Understanding statistics using R
- Using JMP 10
- Using JMP 11
- 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
- Wiley interdisciplinary reviews, Computational statistics

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