Online Program

Modern technology has led to massive increases in information (data) available to businesses and government agencies. Along with this increase in available data, management, employees , researchers and the general public need to be presented with the salient information it provides in a form that is suitable for them to clearly and quickly see the message of any underlying analysis or summary.

Dashboards, available using web-browsers or mobile technology have emerged as an effective medium in which to convey information using appropriate snapshots and trends, and can be tailored for different audiences.

In this tutorial, we will use several case studies to provide a basis for participants to think about how they may effectively construct dashboards for their own audience, with advice on the types of graphs and summaries that can be quickly understood, typical detail that different users may require, layout for web vs mobile technology and the use of group and global filtering. Automation for periodic updating will be achieved using a intuitive GUI interface pipeline, illustrating the ease of updating dashboards (and reports) which previously was manual and often time consuming.

Outline & Objectives

This tutorial is aimed at data scientists and statisticians who need to convey information to audiences beyond technical reports and scientific papers. Along with showing techniques to make dashboards interesting and attractive, we will introduce pipelines to automate the updating of the dashboard as new data becomes available, and guard against unexpected employee attrition and staff changes. No prior experience with Dashboards is required.

The tutorial will be case study based, and several dashboards will be constructed for different purposes. For example, a management dashboard will be constructed, for both webpage and mobile. We will use visualisations such as geo-charts and other standard charts, then filters will be applied to allow users easy access to the information they require.

Dashboards will be constructed with basic summary statistics and extended using more sophisticated models, conveying predictions and trends for policy decision purposes, planning and general interest. In addition, we will use other techniques, such as statistical processs control to produce real time monitoring dashboards that may be beneficial in industry and healthcare services.

About the Instructor

Dr Theo Gazos is the Managing Director of Predictive Analytics Group. Theo has over
25 years of experience building economic and econometric models that isolate and quantify the impact
of changing market dynamics (domestic and international), competition effects and government policy on private and government sector organisations. Theo is passionate about bringing the power of statistics and machine learning to all levels within organisations, and has used his years of experience to develop an interface and user flow within AutoStat® that makes this objective achievable.

Dr Clair Alston-Knox is a Senior Statistician with Predictive Analytics Group (Melbourne
Australia). She had been an research and academic statistician since 1992, with a number of biometric
and statistical consulting positions in government and universities. She joined Predictive Analytics and
the AutoStat Institute in 2018 because her teaching, consulting, advising and ethics committee roles were frequently frustrated by researchers who were very capable of understanding the objective and benefits of statistical or machine learning approaches, but did not have the resources to learn the required platform to enable next level analysis.

Relevance to Conference Goals

Dashboards are a powerful tool for enabling statisticians and data scientists to better communicate and collaborate with colleagues and clients. Constructing a useful dashboard requires skill as an applied statistician, and the collaboration with clients is usually very natural in this setting. Clients and colleagues are able to engage with the messages being displayed in the dashboard, and feedback tends to become very natural. This feedback loop is helpful in developing skills in both data story telling, as the statistician becomes aware of how lay-people interpret visual displays, and it serves to develop potentially lifelong collaborative partnerships through the development of better understandings of how various members of the team contribute to the outcome. The use of dashboards can have a positive effect on the organisation by conveying clear messages to employees in different areas of the operation, and allowing them to see company snapshots and trends in a clear format. This increased understanding allows employees to contribute to dialogue and planning based on a solid understanding of the organisations current position, making statistical contributions very active.

Imagine this: You are a statistical consultant and your new client brings their collected data to you, explains their goals, and asks, “How do I analyze this?” You examine the spreadsheet of data and ask a few questions, only to realize that, although they collected a lot of data, each of the treatments was only applied to one large block, and there isn’t any replication to test the treatment effect! The whole experiment needs to be redone, and the resources that went into this first attempt were wasted.

This is why thoughtful design of the experiment is so critical – it can save you much time, money, and tears!

In this session we will briefly discuss WHY designed experiments are so important, and then we will cover HOW, in JMP, to quickly and easily design your experiment and generate a data table with appropriate order randomizations and with prepopulated model scripts to make analysis a one-click process once you’ve collected the data. We will also cover a few types of common designs, how to branch out into custom designs, and how to compare possible designs to pick the best one for your goals.

Outline & Objectives

Objectives:
Attendees should learn more about:
• How easy designing an experiment can be, even for complex scenarios.
• Common types of experimental designs and how to create them, and the flexibility of a custom optimized design and how to create one.
• How to compare and judge candidate designs, how to explore Power and Sample Size calculations.
• How to match the analysis to the experimental design.

Outline:
Introduction to Design Of Experiments
• What is DOE?
• Conducting Ad Hoc and One-Factor-at-a-Time (OFAT) Experiments
• Why Use DOE?
• Types of Experimental Designs

Factorial Experiments
• Designing Factorial Experiments
• Analyzing Full Factorial

Custom Designs
• Options for Factors
• Quick Overview of Optimality

Case Study
• Defining the Problem and the Objectives
• Identifying the Responses
• Identifying the Factors and Factor Levels
• Identifying Restrictions and Constraints
• Preparing to Conduct the Experiment
• Analysis

About the Instructor

Kevin Potcner is an Academic Ambassador with JMP (a division of SAS), working with professors and researchers to use JMP. Kevin also teaches predictive analytics and data mining in the MBA program at University of San Francisco, and he serves on the Data Science Advisory Board Member at California State University, Fullerton. He has an MS in Statistics from the University of Florida.
Ruth Hummel is also an Academic Ambassador with JMP, supporting the technical needs of professors and instructors who use JMP for teaching and research. Ruth is an author of Business Statistics and Analytics in Practice, 9th edition, and has been teaching and consulting about statistics and analytics for over a decade, at the University of Florida, at the US Environmental Protection Agency, and now at SAS/JMP. She has a PhD in Statistics from The Pennsylvania State University.

Relevance to Conference Goals

This session directly addresses “Theme 2: Study Design and Data Management” and “Theme 3: Implementation and Analysis” by covering the topics of how to design a study, how to compare potential study designs, how to investigate power and sample size concerns, and how to match the analysis of the data to the original experimental design.

Modified: Observational data often come with challenges that the data analyst needs to address. Treatment status or the exposure of interest may not be assigned randomly. Data are sometimes missing not at random (MNAR), which can lead to sample-selection bias. And statistical models for these data often need to account for unobserved confounding.

Join Chuck Huber, Director of Statistical Outreach, as he shows you how you can use standard maximum-likelihood estimation to fit extended regression models (ERMs) that deal with all of these common issues. He will work examples that demonstrate how to account for these observational data problems when they arise individually and when they occur simultaneously.

Outline & Objectives

1. Overview of the potential-outcomes framework for causal inference
o Stable unit treatment value assumption
o Potential-outcome means
o Average treatment effects
o Average treatment effects on the treated
2. Estimating treatment effects
o Using the regress and margins commands
o Using the teffects commands
- Regression adjustment
- Inverse probability weighting
- Propensity score matching
- Covariate distance matching
3. Estimating treatment effects with complications
o Estimating treatment effects while accounting for unobserved confounders
o Estimating treatment effects with sample selection (data missing not at random)

About the Instructor

Joerg Luedicke is a Senior Social Scientist and Statistician at StataCorp LLC. Joerg was the lead developer of Stata's latestdiscrete choice model commands and helped in the development of Stata's teffects suite of commands. Prior to joining StataCorp, Joerg earned a PhD in Sociology from Bielefeld University, Germany.

Relevance to Conference Goals

This proposed practical computing demonstration is primarily relevant in regards to the "Implementation and Analysis" theme of the conference. We will present state-of-the-art statistical methods for estimating treatment effects with observational data. It is also relevant to the "Study Design and Data Management" theme because it is important for researchers to have a good understanding of causal inference methods and treatment effects estimation in the design stage of a study. Because the proposed demonstration shows a number of hands-on examples that include discussion on how to interpret and communicate the results, it also touches on the "Effective Communication" theme of the conference.

WesDaX® is an online analysis and reporting platform created by Westat (www.wesdax.com) that can run from any standard web browser, requires no code writing, and no experience with analysis software. WesDaX supplements project reporting for research projects, and allows staff, clients, collaborators, and stakeholders to run analyses from microdata. The demonstration will begin with some background on WesDaX, what WesDaX can do, and what is unique about WesDaX. A tour through a public data suite will be given, demonstrating analyses of American Community Survey data and Behavioral Risk Factor Surveillance System data. The main objective of the presentation is to provide awareness of the tool, which can be beneficial to the audience and hit on several conference themes, such as educating others about data from surveys, evidence-guided statistical practices, and reproducible evidence. WesDaX analysis results are powered by WesVar (the analytic engine that computes the estimates) and are generated appropriately from complex sample data, with statistical testing. There is an option for advanced confidentiality protection, which protects against table differencing attacks.

Outline & Objectives

The objective of the course is to empower the user to be able to take an in-depth first look at data from sample surveys, while generate statistics that handle complex survey data in variance estimation and statistical testing. An outline of the course is as follows:
1. Introduction to WesDaX®
a. Video
b. Key features
c. Architecture – WesDaX interface, WesVar analytic engine
2. Statistical methods
a. Point estimation
b. Variance estimation
c. Statistical testing
d. Disclosure avoidance
3. Landing page
a. White paper
b. Guide
c. Users
d. Public use suite
e. Demo – BRFSS
4. Exercises
5. Summary
a. Educating others about data from surveys
b. Evidence-guided statistical practices
c. Reproducible evidence
d. How to get started

About the Instructor

Tom Krenzke is a Vice President and Associate Director in Westat’s Statistics and Evaluation Sciences Unit, and has about 30 years of experience in survey sampling and estimation techniques. Mr. Krenzke adds new statistical capabilities by developing software for statistical disclosure control; nonresponse bias analysis; area sampling, and imputation. Mr. Krenzke is a Fellow of the American Statistical Association (ASA) and leads Westat’s Steering Committee on WesDaX (Westat’s real-time online table generator).

Naomi Yount, Ph.D., is an industrial /organizational psychologist and Westat Senior Study Director with more than 15 years of experience in organizational research. She has expertise in a variety of research methodologies, from qualitative interviewing to quantitative analyses of survey and other organizational data. At Westat, Dr. Yount conducts analyses such as psychometric analyses for new or revised surveys and key driver analyses predicting outcomes such as turnover or employee engagement.

Relevance to Conference Goals

WesDaX incorporates best practices in generating tabular statistics and conducting statistical tests from complex survey data without any programming code. As part of a data management toolkit, WesDaX provides an efficient way to disseminate aggregated data to the public. This tool can help statisticians and project managers use data to improve their ability to communicate with and aid customers and organizations, and have a positive impact on your organization. Furthermore, the course will demonstrate benefits related to data preparation for tabulations and will provide illustrative data analysis examples, which focus on a variety of data types from varied applied settings that support evidence-guided statistical practice.