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Session ID: 2023-US-PO-1513

Communication Style in Political Campaigns: Promoting a Personal Connection with an Audience

Robert Magee, Associate Professor of Integrated Marketing Communication, The University of MIssissippi (Ole Miss)

Topic: Basic Data Analysis and Modeling
Level: I

Do some political candidates use first-person (I, we) or second-person pronouns (you, you all) more often in their campaign tweets?

In this course exercise, students learn how to test distributions (analyze, distribution, test probabilities) using a grouping variable (BY). The data set is comprised of tweets (N = 1107) from the early stages of the 2016 U.S. presidential primary season.

First-person pronouns focus on the speaker, or possibly, the group to which the speaker belongs. Second-person pronouns communicate a personal connection to the audience, suggesting that the candidate might be seeking to establish a personal connection.

A chi-squared test of the relationship between political party (Democrat vs. Republican) and the use of first person (Present vs. Absent) is significant. The distributions are tested against a 50-50 distribution to see if Democrats or Republicans are more likely to use first-person pronouns.

The test of the use of second-person pronouns occurs at the canditate level. In this data set, political candidates use the second person in 20% of their tweets. But who uses second person more (or less) than the other candidates? In this part of the exercise, students compare each candidate's use of the second person against the group's 80-20 distribution.

Tests are conducted on tweets from Hillary Clinton, Bernie Sanders, Marco Rubio, and Donald Trump. The results indicated that only some of the candidates used second-person pronouns more often than the group average.
Session ID: 2023-US-PO-1478

Heroes or Zeros: A Product Distribution Analysis Using JMP

Mingyuan Wang, Statistical Engineer, Corning Incorporated

Topic: Quality and Process Engineering
Level: II

Quality is a top concern for most manufacturers. Within the space of an established sampling mechanism, it is vital to be able to tell how likely a set of good samples (hero) actually represents that the entire batch/crate is good. In this presentation, we provide a distribution analysis strategy to assist in answering this question through such methods as modeling, simulation, probability analysis, and data visualization. We also demonstrate how to accomplish this analysis and develop an end-to-end application using the JMP script and user interface. The strategy is evaluated on a real-world induced data set of product samples. It provides a valuable strategy and tool for evaluating the current quality of products and decision making so that the process can be improved.
Session ID: 2023-US-PO-1385

Human Heat Map Designed by the JMP Custom Map Creator

Trent Walker, Medical Student, Ohio State University College of Medicine
Benjamin Kaffenberger, Doctor, The Ohio State University Wexner Medical Center

Topic: Sharing and Communicating Results
Level: I

Disease often shows a predilection for specific site locations. To demonstrate the body regions involved, researchers may use infographics or tables. While tables may be more descriptive and universal to any statistical software, heat maps rapidly communicate disease involvement across a cohort. At our institution, software to portray data into the confines of a human body is not readily available. Additionally, we needed the human body to be segmented into specific regions. While a graphic editor can be used, it is labor intensive, limits generalizability, and cannot be repeated with different data sets.

To create a scalable solution, we used the Custom Map Creator in JMP, an add-in to Graph Builder that allows users to create custom shapes. With this program, we outlined a human body, created anatomic regions, and colored each region based on the percentage of occurrence. The selected color range is scaled to directly correlate with the data. Each anatomical subunit is then filled and shaded by the data in the respective column. Heat maps provide easy interpretation and clearly portray the most involved areas for rapid communication to readers.
Session ID: 2023-US-30MP-1444

Important JMP Tools for Fermentation Processes

Bill Worley, Principal Systems Engineer, JMP
Nicholas Strasser, Technical Support Specialist, BASF Enzymes

Topic: Basic Data Analysis and Modeling
Level: II

The presenters discuss important statistical tools used in monitoring ethanol fermentation using simulated but realistic data. Most, if not all, fermentation processes have long batch cycle times on the order of two to three days. The fermentation process is monitored with measurements made at the start (Prop Stage) and then every eight hours until the final (Drop) stage and ensuing Beer Well stage. Analytic tools and procedures that allow for better and faster decision making and reporting are demonstrated.
Session ID: 2023-US-30MP-1413

Industry Innovation: Blending Modern JMP/Classical Six Sigma - Applied Engineering Statistics (AES)

Patrick Giuliano, Senior Analytical Technical Support Engineer, JMP
Charles Chen, Principal Continuous Improvement Expert, Morrill Learning Center

Topic: Quality and Process Engineering
Level: II

A leading semiconductor manufacturer has developed a novel multidisciplinary program for applied engineering statistics (AES), by incorporating applied problem-solving (6S DMAIC), JMP statistical thinking (STIPS), and core JMP curriculum course content, sponsored by JMP Education. The program's key features culminate in the use of JMP 17 features in powerful, systematic, and practical analytical contexts. The 6S Black Belt curriculum has been seamlessy innovated, based on both AES and JMP curriculum content across DMAIC phases.

The first initiative involved transitioning from Minitab 19 to JMP 16, followed by mapping the JCSA, JGPH, JANR, JMSA, JSPC modules to the DMAIC framework. These JMP core courses help facilitate DMAIC Black Belt Project execution more effectively through AES thinking. Several JMP case studies are demonstrated, including item analysis (Attribute GRR), text mining/data mining hybrid correlation, model-driven multivariate SPC, and group orthogonal supersaturated design.

To train internal instructors to teach Black Belt JMP modules, several advanced JMP curricula (JMP 17) have been created. In addition to internal Black Belt certification, the JMP STIPS Certification Exam was offered, as was the JMP DOE Certification Exam. JMP STIPS courses were made available to any internal employee. The program innovates the traditional 8D project framework by adding JMP 17 tools to make 8D project root cause analysis more objective and data-driven to resolve complex system-level quality challenges . The 2023 March Madness Forum Events, sponsored by 64 presenters, have drawn full attention across the organization, recognize the success of the JMP-oriented AES program.
Session ID: 2023-US-30MP-1380

Quantifying Your Organization's Analytical Maturity: Data-Driven Decision Making to Do Things Better

Peter Polito, Sr. Systems Engineer, JMP
Brady Brady, Principal Systems Engineer, JMP
Ben Rost, Account Manager, JMP
Kyle Bickford, Account Executive, JMP

Topic: Data Exploration and Visualization
Level: I

Data is everything. Every organization, big or small, collects data and knows the more insight they can gain from the data, the more competitive they will be. But what tools does the organization need? What skill sets are necessary for their most valuable asset, their employees? How do they quickly ascertain the level of competency their people have in order to achieve this end?

Enter the JMP Analytical Workflow Survey (JAWS). JAWS is an expeditious tool for organizations at all scales to rapidly and succinctly identify the competency of their people – their strengths and weaknesses and an estimate of time they spend to perform certain analytical tasks – to create a roadmap to achieve organizational goals. By deploying JAWS in your organization, you will gain insight into the current state of your analytical fitness, identify strengths within your organization, and develop targeted action items to address your weaknesses. Perhaps most importantly, it will identify areas in your analytical process that are overly time-consuming or ripe for automation, freeing up valuable brainpower to address more pressing issues.

In this presentation, we walk you through the steps of deploying the JAWS and highlight the incredibly valuable insights one can gain, which will allow your organization to make data-driven decisions efficiently to achieve the analytical ends you desire.
Session ID: 2023-US-PO-1495

Relationship Between the Type of Music and Economic Conditions

Yoon Hye Sun, Student, University of Connecticut
Chanju Oh, Student, University of Connecticut
Trang Nguyen, Student, University of Connecticut
Yu-Ting Cheng, Student, University of Connecticut

Topic: Predictive Modeling and Machine Learning
Level: III

This study investigates the relationship between the trend of music type and the economic situation of a country. The study analyzes a song’s popularity index on Spotify in different countries from 2005-2019, as well as economic indicators such as GDP, inflation rate, unemployment rate, and happiness index. The research utilizes statistical techniques such as correlation analysis and regression analysis to examine the relationship between these variables.

We assume that there is a significant correlation between the trend of music type and the economic situation of a country. During times of economic downturn, we presume there is an increase in the popularity of upbeat and energetic genres such as electronic dance music, while during economic growth periods, more soulful, introspective, thoughtful music genres tend to dominate the charts. The study provides valuable insights into the cultural and economic factors that influence the music industry and can assist music producers and marketers in understanding consumer behavior so that they can make more informed decisions.
Session ID: 2023-US-PO-1404

Restrictions on Public Resources for Abortions: Impacts on Fertility Rates Based on Maternal Age

Renita Washburn, PhD Student, University of Central Florida
Dr. Mary Jean Amon, Assistant Professor, University of Central Florida

Topic: Basic Data Analysis and Modeling
Level: I

This research examines impacts to United States fertility rates as a function of state legislative restrictions on the use of public resources (e.g., Medicaid funds) for abortions. Data from LawAtlas and the Centers for Disease Control's WONDER databases were used to model 2021 fertility rates based on maternal age group and abortion legislation. A two-way ANOVA of rank transformed fertility rates was used to identify impacts of legislative restriction across six maternal age groups. Latent class analysis was used to identify patterns in state abortion restrictions composition and their relationship with fertility rates based on age.

It was revealed that the impact of abortion restrictions targeting public resources on fertility rates varied based on maternal age; for example, women ages 15-29 had lower fertility rates when there were no restrictions. Additionally, legislative restrictions on multiple categories of public resources were associated with higher state fertility rates. The poster includes visualization of summary statistics and findings with maps and charts. The poster demonstrates a method for addressing unbalanced data using transformation, as well as the use of latent class analysis with binary categorical variables.
Session ID: 2023-US-30MP-1402

Retrieving Arbitrary True Part Distributions from Measured Distributions and Gauge Characteristics

Jerry Fish, Systems Engineer, JMP

Topic: Automation and Scripting
Level: II

Part distributions are easy to measure: parts are built, an operator measures the parts with a gauge, and the results are assembled into a measured part distribution (MPD).

But the resulting distribution is contaminated by errors associated with the measurement system. Random errors, gauge bias, and linearity problems all contribute to inaccuracies in measuring the true part values, so the individual values can never be truly known.

However, if we had a way to estimate the true part distribution (TPD), we could compare it to the MPD and calculate the impact (cost) associated with using the imperfect gauge in terms of Type 1 and Type 2 errors.

It is trivial to estimate the TPD from an MPD if the gauge creates simple normally distributed errors around a normally distributed TPD (i.e., simply subtract variance of gauge from MPD variance to get TPD variance). But what if the gauge has linearity problems? Or what if the TPD has a non-normal shape?

This paper describes a new JSL script for determining an arbitrary (i.e., non-parametric) TPD from an arbitrary MPD and associated gauge performance characteristics. The resulting TPD can then be fed to a second script to determine production costs associated with the imperfect gauge and setting guardbands to optimize economics of the gauge errors. Performance of the estimation routine is evaluated, in terms of shape of TPD, various gauge characteristics, and resolution of distributions.
Session ID: 2023-US-30MP-1415

Take a JAAB at Add-In Building: Using GitHub Actions to Streamline Deployment of JMP Tools

Nathan Clark, Staff Systems Scientist, IDEXX Laboratories
Sage Darling, Product Transfer Analytics Engineer, IDEXX Laboratories

Topic: Automation and Scripting
Level: II

As JSL use has grown, so has the need for various levels of control and maintenance. Keeping version control local, Add-In Manager was a huge game changer to easily manage, package, and release many add-ins. Since so much code is stored in the cloud, a process was created using GitHub actions and a Python script to package and deploy add-ins.

This presentation covers JAAB (Just Another Add-In Builder), which uses GitHub actions to automatically package and deploy add-ins with common files from other repositories upon release creation. Using a customizable Python script and configuration file, developers have options to augment deployment to their needs. Gone are the days of the developer needing to manage .jmpcust, metadata, or .def files. But the add-in creation process doesn't stop here! So tune into this presentation to learn more!
Session ID: 2023-US-30MP-1421

Use JMP Item Analysis Platforms to Detect the Exam Difficulty and Discrimination

Chih Ching Wu, Engineer, GCI
Wayne Chou, Engineer, Customer Quality, Applied Materials Corp.
Yuchen Lai, Customer Quality Engineer, GCI

Topic: Consumer and Market Research
Level: II

This project studied JMP Black Belt exam patterns among students to understand exam difficulty and discrimination across platforms in JMP 16. Through preliminary scoring distribution, the instructor can identify each exam question's difficulty and discrimination. The statistical parameters can then be used to determine which questions should be included in which level of examination, such as Green Belt or Black Belt. By comparing three different Black Belt STIPS exams with a dual plot, questions can be designed for different types of exams.

The characteristic curves are related the difficulty and discriminatory properties of each question and are then compared with each student’s ability and probability of answering correctly. Dual plots use the same scale to show the relative difficulty for a group of students' capability when answering each question. Parameter estimates are used to measure the difficulty and discrimination of each question. The sample questions in three STIPS exams use four kinds of questions and four levels of difficulty and discrimination. By using JMP Item Analysis platform, instructors can adapt any learning program and their instruction to accommodate different levels of students according to the difficulty and discrimination of test questions.