Search

Loading [MathJax]/jax/output/SVG/config.js

Filter Results
Location
- Campus Bookshelves (2)
Classification
- Article type
  - Book or Unit
  - Chapter
  - Section or Page
  - N/A
  - N/A
- Author
  - David Lane
  - Alexander Aue
  - John H. McDonald
  - OpenStax
  - OpenIntro Statistics
  - Debashis Paul
  - Larry Green
  - Diane Kiernan
  - Alexey Shipunov
  - Peter Kaslik
  - Kathryn Kozak
  - David Lilja
  - Kristin Kuter
  - Foster et al.
  - Jenkins-Smith et al.
  - Grinstead & Snell
  - Russell A. Poldrack
  - Thomas K. Tiemann
  - Danielle Navarro
  - Jonathan A. Poritz
  - Kyle Siegrist
  - Paul Pfeiffer
  - Matthew J. C. Crump
  - Michelle Oja
  - Rachel Webb
  - Alex Reinhart
  - Maurice A. Geraghty
  - Anonymous
  - Erich C Fein, John Gilmour, Tayna Machin, and Liam Hendry
  - Gordon E. Sarty
  - Penn State's Department of Statistics
  - Mark Greenwood
  - Yang Lydia Yang
  - Bill Pelz
  - Valerie Watts
  - Nancy Ikeda
  - Linda R. Cote, Rupa G. Gordon, Chrislyn E. Randell, Judy Schmitt, and Helena Marvin
  - Klaire Somoray
  - Hannah Seidler-Wright
  - Michael R Dohm
  - Christina R. Peter
  - Lumen Learning
  - Roger Clark
  - Mikaila Mariel Lemonik Arthur
  - Mikaila Mariel Lemonik Arthur and Roger Clark
  - Yvonne Anthony
  - Jaeyong Choi
- Cover Page
  - yes
  - TOC Only
  - Compile but don't publish
- License
  - Public Domain
  - CC BY
  - CC BY-SA
  - CC BY-NC-SA
  - CC BY-ND
  - CC BY-NC-ND
  - GNU GPL
  - All Rights Reserved
  - CC BY-NC
  - GNU FDL
- Show TOC
  - yes
  - no
- Embed Jupyter
  - python
  - octave
  - r
  - sagemath
- Transcluded
  - yes
- OER program or Publisher
  - College of the Canyons - Zero Textbook Cost Program
  - ASCCC OERI Program
  - CK-12
  - OpenStax
  - GALILEO
  - OpenSUNY
  - MIT OpenCourseWare
  - Virginia Tech Libraries' Open Education Initiative
  - The Publisher Who Must Not Be Named
  - eCampusOntario
  - WAC Clearinghouse
  - BC Campus
  - Lumen
  - Open Oregon
  - OpenStax CNX
  - PDXOpen
  - Evergreen Valley College
- Autonumber Section Headings
  - title with space delimiters
  - title with colon delimiters
  - title with dash delimiters
- License Version
  - 1.0
  - 1.3
  - 2.0
  - 2.5
  - 3.0
  - 4.0
Include attachments
- Content Type
  - Document
  - Image
  - Other

9.1.2: Paired Data
https://stats.libretexts.org/Courses/American_River_College/STAT_300%3A_My_Introductory_Statistics_Textbook_(Mirzaagha)/09%3A_Hypothesis_Testing_about_Population_Mean_and_Proportion/9.01%3A_Inference_for_Numerical_Data/9.1.02%3A_Paired_Data
Two sets of observations are paired if each observation in one set has a special correspondence or connection with exactly one observation in the other data set. To analyze paired data, it is often us...Two sets of observations are paired if each observation in one set has a special correspondence or connection with exactly one observation in the other data set. To analyze paired data, it is often useful to look at the difference in outcomes of each pair of observations.
8.1.2: Paired Data
https://stats.libretexts.org/Courses/American_River_College/STAT_300%3A_My_Introductory_Statistics_Textbook_(Mirzaagha)/08%3A_Finding_Confidence_Interval_for_Population_Mean_and_Proportion/8.01%3A_Inference_for_Numerical_Data/8.1.02%3A_Paired_Data
Two sets of observations are paired if each observation in one set has a special correspondence or connection with exactly one observation in the other data set. To analyze paired data, it is often us...Two sets of observations are paired if each observation in one set has a special correspondence or connection with exactly one observation in the other data set. To analyze paired data, it is often useful to look at the difference in outcomes of each pair of observations.

Search

Text Color

Text Size

Margin Size

Font Type

Support Center

How can we help?