img Leseprobe Leseprobe

Preparing Data for Analysis with JMP

Robert Carver

EPUB
ca. 28,99
Amazon iTunes Thalia.de Weltbild.de Hugendubel Bücher.de ebook.de kobo Osiander Google Books Barnes&Noble bol.com Legimi yourbook.shop Kulturkaufhaus ebooks-center.de
* Affiliatelinks/Werbelinks
Hinweis: Affiliatelinks/Werbelinks
Links auf reinlesen.de sind sogenannte Affiliate-Links. Wenn du auf so einen Affiliate-Link klickst und über diesen Link einkaufst, bekommt reinlesen.de von dem betreffenden Online-Shop oder Anbieter eine Provision. Für dich verändert sich der Preis nicht.

SAS Institute img Link Publisher

Naturwissenschaften, Medizin, Informatik, Technik / Informatik, EDV

Beschreibung

Access and clean up data easily using JMP®!

Data acquisition and preparation commonly consume approximately 75% of the effort and time of total data analysis. JMP provides many visual, intuitive, and even innovative data-preparation capabilities that enable you to make the most of your organization's data.

Preparing Data for Analysis with JMP® is organized within a framework of statistical investigations and model-building and illustrates the new data-handling features in JMP, such as the Query Builder. Useful to students and programmers with little or no JMP experience, or those looking to learn the new data-management features and techniques, it uses a practical approach to getting started with plenty of examples. Using step-by-step demonstrations and screenshots, this book walks you through the most commonly used data-management techniques that also include lots of tips on how to avoid common problems.

With this book, you will learn how to:

  • Manage database operations using the JMP Query Builder
  • Get data into JMP from other formats, such as Excel, csv, SAS, HTML, JSON, and the web
  • Identify and avoid problems with the help of JMP’s visual and automated data-exploration tools
  • Consolidate data from multiple sources with Query Builder for tables
  • Deal with common issues and repairs that include the following tasks:
    • reshaping tables (stack/unstack)
    • managing missing data with techniques such as imputation and Principal Components Analysis
    • cleaning and correcting dirty data
    • computing new variables
    • transforming variables for modelling
    • reconciling time and date
  • Subset and filter your data
  • Save data tables for exchange with other platforms

Kundenbewertungen

Schlagwörter

analytics, data cleaning, data management, data wrangling, missing data