If you work with data, you should attend this webinar. Real life data is not pretty. It is messy and often incomplete. There are many ways to handle missing data and many of these ways are not the best. There are better ways to handle missing-ness in your data. Not perfect, but better.

Just removing records (listwise deletion) with missing data can reduce the power of your study and result in Type II error (when the effects are truly there, but you don’t have enough power to achieve statistical significance). And, many of the adjustments that researchers use for handling missing data introduce bias into the data. For instance, one of the most common problems with many of the techniques is a reduction in standard error of the estimates, which results in inflated Type I errors (seeing significant findings when they do not truly exist).

Good power and low bias are hard to control to begin with. It is important to learn how to handle missing-ness to make efficient use of our data to achieve accurate and precise results. Some knowledge of linear regression is desired.

This 6-hour virtual seminar includes a presentation of the steps and techniques used to quantify variability in manufacturing processes, and to assure quality products.

The concepts and information presented will be mainly concerned with statistical process control: obtaining monitoring information (data) that is objective, unbiased, and useful for decision making. An emphasis will be placed on the set-up and use of control charts.

The objective of the seminar is to provide information that can be used immediately by personnel involved in production operations, and by supervisors and management in decision making. Although the presentation involves use of statistical techniques, presentation of statistical theory will be limited to only what is needed by the attendees to understand and implement processes and monitoring tools within the statistical framework.

Presented examples will include an emphasis on the manufacturing processes and quality assurance needs of product in the medical device and pharmaceutical industries.

Process control is constantly evolving. Therefore, historical concepts, current trends and regulatory requirements will be discussed. The presentation of statistical charts and analyses, graphical techniques for planning, trouble-shooting and problem solving will also be presented.

Statistics is a useful decision-making tool in the clinical research arena. When working in a field where a p-value can determine the next steps on development of a drug or procedure, it is imperative that decision makers understand the theory and application of statistics.

Many statistical softwares are now available to professionals. However, these softwares were developed for statisticians and can often be daunting to non-statisticians. How do you know if you are pressing the right key, let alone performing the best test?

This seminar provides a non-mathematical introduction to biostatistics and is designed for non-statisticians. And it will benefit professionals who must understand and work with study design and interpretation of findings in a clinical or biotechnology setting.

The focus of the seminar is to give you the information and skills necessary to understand statistical concepts and findings as applies to clinical research, and to confidently convey the information to others.

Emphasis will be placed on the actual statistical (a) concepts, (b) application, and (c) interpretation, and not on mathematical formulas or actual data analysis. A basic understanding of statistics is desired, but not necessary.

In today’s data-driven world, making informed decisions is crucial for success. Design of Experiments (DoE) provides a structured approach to investigate relationships between variables, eliminating guesswork of what is important, leading to reliable outcomes. DoE enhances efficiency by allowing simultaneous examination of multiple factors, saving time and providing a comprehensive understanding of systems.

Professionals across various fields greatly benefit from using DoE. Scientists and researchers can conduct rigorous experiments, engineers can optimize manufacturing processes, pharmaceutical professionals can develop new treatments, and marketers can understand consumer behavior better. Mastering DoE is essential for effective decision-making, efficiency, and innovation.

To harness the full potential of DoE, consider attending our 2-Day Virtual Seminar on Design of Experiments. Elaine Eisenbeisz, Owner of Omega Statistics, will provide practical insights and demonstration of DOE models. Minitab statistical software will be used in the training. Whether you’re a scientist, engineer, marketer, or industry professional, this seminar will equip you with the skills to implement DoE effectively, driving success in your field.