Plant Sciences

Accessibility


| Share

2020 Statistical Experimental Design Short Course

The Introduction to Statistical Experimental Design Short Course is a three-day class that will prepare participants to plan and analyze field and laboratory studies. The course is intended for individuals conducting research on agricultural products in the field or laboratory and field agronomists.
When Feb 25, 2020 08:00 AM to
Feb 27, 2020 02:00 PM
Where NDSU Loftsgard Hall, Room 380
Contact Name
Contact Phone 701-231-5384 (O), 701-318-4839 (C)
Add event to calendar vCal
iCal

 

presents

Introduction to Statistical Experimental Design Short Course

February 25-27, 2020
Loftsgard Hall, Room 380

North Dakota State University
Fargo, North Dakota

Overview

The Introduction to Statistical Experimental Design Short Course is a three-day class taught by North Dakota State University Department of Plant Sciences professor and experimental design instructor Dr. Richard Horsley and large database breeding pipeline manager Dr. Ana Heilman Morales. The course will prepare participants to plan and analyze field and laboratory studies. Topics include a statistical review, experiment planning, hypothesis testing, mean comparison tests, regression and correlation of several common experimental designs, and data QAQC and visualizations.

The course is intended for individuals conducting research on agricultural products in the field or laboratory and field agronomists. Background needed for the course is knowledge of basic math. 18 CEUs are available for Certified Crop Advisers, agronomists and soil scientists completing the course.

Instructors

Dr. Richard Horsley is professor and barley breeder in the Department of Plant Sciences at North Dakota State University. He joined the faculty in 1988 and is currently head of the department. Dr. Horsley received a B.S. in Agronomy from the University of Minnesota, an M.S. in Agronomy from NDSU, and a Ph.D. in Crop and Weed Sciences from NDSU. Dr. Horsley has taught a graduate level course in experimental design yearly since 1987 and the Introduction to Statistical Experimental Design Short Course in 2008, 2010, 2013 and 2018.

Dr. Ana Heilman Morales is a plant biologist and data scientist. She has worked as a breeding pipeline database manager in the Department of Plant Sciences since 2016, where she supports plant breeders/faculty, staff and students with data analytic needs,  leads the expansion of technology transference and deployment of software products that support NDSU breeding operations, and serves as departmental IT liaison to technical planning groups in the NDSU Information Technology and Ag Communications Divisions. She received her M.Sc. in Biological Sciences from University of Puerto Rico at Mayagüez and a Ph.D. in Plant Sciences from North Dakota State University.

Registration

Cost of registration for the course is $600. This includes course materials, lunches and coffee breaks. Other meals are on your own. Travel expenses and hotel are your responsibility.

Enrollment for this course is limited.

Register Online

Schedule

Classes will run from 8:00a.m. on Tuesday, Feb. 25 through 2:00p.m. on Thursday, Feb. 27.

Location

The course will be held in Loftsgard Hall room 380 on the NDSU campus in Fargo, ND.

Lodging

A block of rooms has been reserved at the Radisson Hotel Fargo at the rate of $109 plus tax per night. These rooms are available at this rate until January 25, 2020. To reserve a room, call the hotel directly and ask for the NDSU Department of Plant Sciences room block.

Radisson Hotel Fargo
201 5th St. N.
Fargo, North Dakota, 58102
Phone: 701-232-7363
www.radissonfargo.com

Contact Person

Please contact Karen Hertsgaard with questions at 701-231-5384 (O), 701-318-4839 (C) or karen.hertsgaard@ndsu.edu.

Topic Outline

Statistical Review

Planning Experiments

Hypothesis Testing

Comparisons Involving Two Sample Means

Completely Random Design

Mean Comparison Tests

Randomized Complete Block Design

Lattice Design

Different Arrangements Used in Experimental Designs

Combined Analysis of Experiments

Regression and Correlation

Data QAQC and Visualizations

Creative Commons License
Feel free to use and share this content, but please do so under the conditions of our Creative Commons license and our Rules for Use. Thanks.