Change-Point Analyzer


Editor's PickState of the art tool for trending data to detect a change.   An important tool as part of problem investigations to determine the start of the problem.

Try It

Version 2.3 for Windows XP, Vista, 7, 8 and 10, including the 64-bit versions.


Download Full Version of Software – Try it Free for 30 Days

A full version of the software can be downloaded above and used for free for a 30-day trial.  After 30 days, if you want to continue using the software, you must purchase a license.  The file cpa12.exe will be downloaded.  Executing this file will install the software on your system including the tutorials.  Additional install steps for the Help system.

Purchasing Software

To continue using the software after the 30-day trial period, you must purchase a license for the software.  A user name and license code will be emailed to you along with further instructions.   You also get free upgrades for 1 year.  Site licenses are also available.

Change-Point Analyzer Site Licenses


Change-Point Analyzer is a shareware software package for analyzing time ordered data to determine whether a change has taken place.  It detects multiple changes and provides both confidence levels and confidence intervals for each change.  The results are clearly displayed in table form and supplemented by easy to interpret plots.  It can be used with all types of data: pass/fail, individual values, averages, ranges, standard deviations, normal, nonnormal and ill-behaved data like bioburden and particulate counts.  It can be used to trend complaints, inventory turns, sales, particulate counts and just about anything else.

Change-Point Analyzer is simple to use, even for someone with no experience with control charting.  It can be learned in about 15 minutes by following the tutorials provided below.  It automatically verifies assumptions, checks for outliers and guides the user in handling such special cases.  It even comes with a Excel Add-In making it possible to perform the analysis directly from Excel.  For those in FDA regulated industries, it comes complete with a validation package.

Change-Point Analyzer is an important problem-solving tool which can be used to complement real-time control charts.   Analyzing control charting data using Change-Point Analyzer will help to better isolate the time of a change, help to identify more subtle changes missed by the control chart, and expose false detections.

When performing a one-time analysis on historical data, a change-point analysis is preferred to control charting, especially when you are dealing with large datasets.  When performing such analyses, a change-point analysis has numerous advantages over a control chart.

Change-Point Analyzer was developed by Dr. Wayne Taylor.  It utilizes state of the art techniques including CUSUM charts and bootstrap analysis.


  • Detects multiple changes
  • For each change:
    • Provides a confidence level that change occurred
    • Estimates the time of the change and provides a corresponding confidence interval
  • Handles all types of data: pass/fail, individual values, averages, ranges, standard deviations, normal, nonnormal, counts, and ill-behaved data like bio and particulate counts.


  • More powerful than control charts at detecting sustained changes.
  • Better characterizes changes including detection of multiple changes, providing associated confidence levels, and providing confidence intervals for the times of the changes.
  • Reduces the number of false detections when dealing with large data-sets.
  • Robust to outliers
  • Flexible:  The same procedure works for all types of data.
  • Simpler to use and easier to interpret

Editor's PickAwards

  • Change-Point Analyzer received ZDNet’s four-star rating.

Further Information

Other Information on Change-Point Analysis


Applications of change-point analysis include trending of process and quality data, problem solving by identifying the start of the problem, health and medicine, environment, climate change, fraud detection, defense and security, infectious diseases, purchasing transactions and much more.  Below are articles using change-point analysis from a few of our customers.

Health and Medicine

  • The Impact of a  Celebrity Promotional Campaign on the use of Colon Cancer Screening: The Katie Couric Effect – Peter Cram, A. Mark Fendrick, John Inadomi, Mark E. Cowen, Daniel Carpenter, Sandeep Vijan – Archives of Internal Medicine (July 14, 2003), Vol. 163, 1601-1605.  A change-point analysis was performed of colon screenings with the primary change-point corresponding to Katie Couric’s March 2000 Today Show colorectal cancer awareness campaign on colonoscopy rates.
  • A Case for Revisiting Peer Review: Implications for professional self-regulation and quality improvement – Terry E. Hill , Peter F. Martelli, Julie H. Kuo – Plos One – Quality improvement in healthcare has often been promoted as different from and more valuable than peer review and other professional self-regulation processes.  Our objectives were to (1) evaluate the relative contributions of professional accountability and quality improvement interventions to an observed decrease in population mortality and (2) explore the organizational dynamics that potentiated positive outcomes.
  • Movement-Related Changes in Synchronization in Human Basal Ganglia – Michael Cassidy, Paolo Mazzone, Antonio Oliviero, Angelo Insola, Pietro Tonali, Vincenzo Di Lazzaro and Peter Brown – Brain (2002 June), 125 (Pt 6), 1235-46.  Brain is a prestigious neuroscience journal.  A change-point analysis was used to analyze the reaction of the brain track to certain stimulus allowing comparisons between treated and untreated patients with Parkinson’s disease.
  • 300-Hz Subthalamic Oscillations in Parkinson’s Disease – G. Foffani, A. Priori, M. Egidi, P. Rampini, F. Tamma, E. Caputo, K. A. Moxon, S. Cerutti and S. Barbieri – Brain (2003 August 21), 126, 2153-2163.  Brain is a prestigious neuroscience journal.  A change-point analysis was used to analyze changes in the 300-Hz subthalamic oscillations in response to a motion stimulus so that the resulting changes could be compared before and after the administration of levodopa.
  • Was King John of England bipolar?: a medical history using mathematical modeling – Gillespie, Janet, Ph.D. Thesis, University of St Andrews.  Using primary historical sources and published analyses, bipolar symptoms were identified and their temporal relationship to the ICD-10 compliant CPA periods evaluated.  The pattern of changes in King John’s activity is highly suggestive of bipolar disorder with primary historical sources describing synchronous bipolar behavior.  Change Point Analysis merits greater consideration when analyzing time-based data, as does the use of activity as an objective marker of human behavior.
  • Technical Proficiency in Hand-Assisted Laparoscopic Colon and Rectal Surgery – Determining How Many Cases Are Required to Achieve Mastery, Rajesh Pendlimari, MBBS; Stefan D. Holubar, MD; Eric J. Dozois, MD; David W. Larson, MD; John H. Pemberton, MD; Robert R. Cima, MD, MA.  Change-Point Analysis (CUSUM) was used to define the number of cases required to effect improvement in operative time. Cases before and after the change point were considered as being in the “learning period” and “skilled period.”
  • A quasi-experimental test of an intervention to increase the use of thiazide-based treatment regimens for people with hypertension, Carol M Ashton, Myrna M Khan, Michael L Johnson, Annette Walder, Elizabeth Stanberry, Rebecca J Beyth, Tracie C Collins, Howard S Gordon, Paul Haidet, Barbara Kimmel, Anna Kolpakchi, Lee B Lu, Aanand D Naik, Laura A Petersen, Hardeep Singh and Nelda P Wray.  Statistical process control charts, change point analyses, and before-after analyses were used to estimate the intervention’s effects.
  • Estimation of the distribution of change-points with application to fMRI data, John A D Aston and Claudia Kirchz. Using the developed methods, a large study of resting-state fMRI data is conducted to determine whether the subjects undertaking the resting scan have
    non-stationarities present in their time courses.

Process and Quality Data


Computer Science


  • Variability Detection by Change-Point Analysis, Seo-Won Chang and Yong-Ik Byun, Department of Astronomy, Yonsei University, Seoul, Korea.  We confirm that the CPA is a powerful method to determine whether a change has taken place in time series dataset.  From our re-analysis of MMT transit survey data, we found previously unknown evidence about stellar variability, including a total of 606 flare events, 18 eclipsing-like features, and 3 transit-like features.

Environment and Climate Change


Scroll to Top