You can watch the video ``Master JMP: Analyzing date and time data.''
- This is an on-demand version of the webinar held in June 2022. The seminar content has been reorganized for on-demand use, and introduces JMP's effective visualization of date/time data and time-series data analysis.
- The video consists of two chapters (Chapter1 and Chapter2), and each chapter is divided into three sections (Section1 to Section3).
- The JMP format data (.jmp) used in the video can be downloaded from the link at the top of this page.
*This seminar uses "JMP 16.2," the latest version at the time of video creation (August 2022).
*During the webinar, we introduced the function of "Function Data Explorer," but it is not included in this on-demand video. We plan to introduce the functions of "Function Data Explorer" on another occasion.
Chapter 1: How to handle date/time data with JMP and how to effectively visualize it
We will explain the basics of JMP's date/time data format, and based on that, we will explain examples of visualizing date/time data using JMP. Lines, smooth lines, regression lines, bar graphs, heat maps, and bubble plots are used as visualization graphs.
・Chapter1 Section1 : Basics regarding JMP date/time data (10 minutes 20 seconds)
・Chapter1 Section2 : Analysis example 1 Daily data (13 minutes 09 seconds)
・Chapter1 Section3 : Analysis example 2 Annual, monthly data (14 minutes 48 seconds)
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Chapter 2: Time series prediction using JMP ~ARIMA model, state space smoothing model~
This article explains how to use JMP to fit a model to time-series data and predict future behavior. JMP has two platforms for time-series forecasting: "time-series analysis" and "time-series forecasting." We will explain the characteristics of each platform and explain how to actually use time-series data to create time-series models. We will demonstrate how to fit, select the optimal model, and make predictions from the fitted model.
・Chapter2 Section1 : Introduction (5 minutes 57 seconds)
・Chapter2 Section2 : Fitting ARIMA model by “time series analysis” (18 minutes 35 seconds)
・Chapter2 Section3 : Fitting a state space smoothing model using “time series prediction” (18 minutes 03 seconds)
The next "Let's Master JMP" series will be a two-part series on October 4th and 12th, and will cover principal component analysis and cluster analysis.
If you are interested, please join us.
Details and application (free)
Master JMP Principal Component Analysis/Cluster Analysis | JMP
