prediction interval

14 papers and 2 presentations; 15 titles.

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Conference ×	Year ×	Title ×	Authors	Extras	Pages
PharmaSUG	2026	Women Leadership in AI-Driven Clinical Programming: Navigating Intersectionality and Innovation	Lida Gharibvand	source	31
SESUG	2025	Time Series Analysis of Quarterly Air Traffic Passenger Counts at San Francisco International Airport	Trenzy Odero, Mostafa Zahed		16
PHUSE EU	2023	Revolutionising Data Exploration: An Interactive Journey Through R Shiny [presentation]	Valentin Legras	source	30
SGF	2021	SAS Time Series Analysis & Forecasting (TSAF) at the Canada Revenue Agency (CRA), with COVID impacts	Jason A. Oliver	source presentation	22
SESUG	2019	A Random Forest Example of the Boston Housing Data using the Base SAS and the PROC_R macro in SAS Enterprise Guide	Melvin T. Alexander	source	18
MWSUG	2013	Dealing with Data below the Detection Limit: Limit Estimation and Data Modeling	Mark Bailey, Diane K. Michelson		21
SCSUG	2011	An Introductory Look at the Situational Context Inherent in the RBI Using Two Modeling Approaches	Ryan Sides	source	14
WUSS	2011	Hunting for Columbus' Eggs in the SAS Programming World: A Guidance to Creative Thinking for SAS Programmers	Alice M. Cheng	source	12
NESUG	2005	Adopting New Techniques: Statistical Procedures to Determine the Agreement between two Laboratory Methods	Ashley Peterson, Larry W. Douglass		13
SUGI	2003	Case Studies in Time Series	David A. Dickey	source	9
SEUGI	2001	Evolution of the Data Warehouse at the Russian Railways [presentation]	Igor Movchikov	source	24
SUGI	2000	Improved Confidence Interval on the Mean Response in Simple Linearizable Regression	Mark Carpenter, Pandu P. Kulkarni	source	6
WUSS	1999	Exploring Enzyme-Linked Immunosorbent Assay (ELISA) Data with the SAS Analyst Application	Stewart L. Fossceco, Nathan A. Curtis	source	9
SUGI	1999	Exploring Enzyme-Linked Immunosorbent Assay (ELISA) Data with the SAS Analyst Application	Stewart L. Fossceco, Nathan A. Curtis	source	9
SUGI	1999	Statistical Graphics	David A. Dickey	source	8
SUGI	1997	Using Linear Regression and the Gibbs Sampler to Estimate the Probability of a Part Being Within Specification	Brenda Cantell	source	6

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