Supplies background necessary to design and analyze field and laboratory experiments. Focus on statistical analysis for biological scientists. Primary emphasis on analysis of variance, regression, and experimental design. Computer-assisted analysis experience provided.

Prerequisites: a course in basic statistical analysis.

Graduate level independent project or research, unrelated to students' thesis or dissertation.

With lab. The acquisition, translocation, distribution, and function of the essential inorganic elements in plants. Genetic control of plant nutrition and ecological adaptation to nutritional variables. Diagnosis of plant nutritional disorders. Prerequisites: STOCKSCH 105 and STOCKSCH 108, and either CHEM 110 or 111 or equivalent courses.

With lab. The acquisition, translocation, distribution, and function of the essential inorganic elements in plants. Genetic control of plant nutrition and ecological adaptation to nutritional variables. Diagnosis of plant nutritional disorders. Prerequisites: STOCKSCH 105 and STOCKSCH 108, and either CHEM 110 or 111 or equivalent courses.

With lab. The acquisition, translocation, distribution, and function of the essential inorganic elements in plants. Genetic control of plant nutrition and ecological adaptation to nutritional variables. Diagnosis of plant nutritional disorders. Prerequisites: STOCKSCH 105 and STOCKSCH 108, and either CHEM 110 or 111 or equivalent courses.

An overview course designed to provide First-Year students with information, opportunities, and skills to ease their transition into college and build a successful foundation necessary to reach their educational goals.

The non-honors version of the course covers basics of probability, random variables, binomial and normal distributions, central limit theorem, hypothesis testing, and simple linear regression. Through additional assigned readings and weekly discussions, the 1-credit honors colloquium will prepare students to conduct basic statistical studies by expanding on the material covered in Linear Regression and introducing the basics of ANOVA and analysis of categorical data, using the statistical package Minitab.

In this course, we will apply several novel machine learning algorithms, including normalization methods, classification and regression analysis on cancer patient data sets to arrive at personalized cancer treatments. We will develop several algorithms for analyzing cancer data sets, including gene expression data sets. We will review, develop, and evaluate some computational biology methods. We will implement most of these methods in Python. Although programming skills, machine learning, or computational biology background are preferred, they are not required for this course.

Probability theory, including random variables, independence, laws of large numbers, central limit theorem; statistical models; introduction to point estimation, confidence intervals, and hypothesis testing. Prerequisite: advanced calculus and linear algebra, or consent of instructor.

Regression is the most widely used statistical technique. In addition to learning about regression methods this course will also reinforce basic statistical concepts and expose students (for many for the first time) to "statistical thinking" in a broader context. This is primarily an applied statistics course.