You can watch the video "Mastering JMP: Analyzing Date and Time Data".
- This is an on-demand version of a web seminar held in June 2022. The seminar content has been restructured for on-demand viewing, and it introduces the effective visualization functions of date and time data and the time series data analysis functions of JMP.
- The video consists of two chapters (Chapter 1 and Chapter 2), and each chapter is divided into three sections (Section 1 to Section 3).
- 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 available at the time of video creation (August 2022).
*While the "Function Data Explorer" feature was introduced during the web seminar, it is not included in this on-demand video. The "Function Data Explorer" feature will be introduced at a later date.
Chapter 1:How to handle date and time data using JMP, and effective visualization methods.
This document explains the basics of JMP's date and time data format, and then demonstrates examples of date and time data visualization using JMP. The visualization graphs used will include line graphs, smoothed lines, regression lines, bar graphs, heatmaps, and bubble plots.
Chapter 1, Section 1 : Basic information on date and time data in JMP (10 minutes 20 seconds)
Chapter 1, Section 2 : Analysis Example 1 - Daily Data (13 minutes 09 seconds)
Chapter 1, Section 3 : Analysis Example 2 - Annual and Monthly Data (14 minutes 48 seconds)
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Chapter 2:Time series forecasting using JMP: ARIMA model, state-space smoothing model.
This presentation explains how to use JMP to fit models to time series data and predict future behavior. JMP offers two time series forecasting platforms: "Time Series Analysis" and "Time Series Forecasting." We will explain the characteristics of each platform and demonstrate how to fit a time series model to actual time series data, select the optimal model, and make predictions based on the fitted model.
Chapter 2 Section 1 : Introduction (5 minutes 57 seconds)
