Apply the sampling distribution of the sample mean as summarized by the Central Limit Theorem (when appropriate). In particular, be able to identify unusual samples from a given population.

This is an introductory course in biochemistry, designed for both biology and chemical engineering majors.

A consistent theme in this course is the development of a quantitative understanding of the interactions of biological molecules from a structural, thermodynamic, and molecular dynamic point of view. A molecular simulation environment provides the opportunity for you to explore the effect of molecular interactions on the biochemical properties of systems. Topics covered include: Protein Function, Structure and Function of Carbohydrates, Lipids and Biological Membranes, Metabolism, Nucleic and Acid and Biochemistry.

Learning Objectives: 1).Determine point estimates in simple cases, and make the connection between the sampling distribution of a statistic, and its properties as a point estimator.
2). Explain what a confidence interval represents and determine how changes in sample size and confidence level affect the precision of the confidence interval.
3). Find confidence intervals for the population mean and the population proportion (when certain conditions are met), and perform sample size calculations.

1). Summarize and describe the distribution of a categorical variable in context.
2). Generate and interpret several different graphical displays of the distribution of a quantitative variable (histogram, stemplot, boxplot).
3). Summarize and describe the distribution of a quantitative variable in context: a) describe the overall pattern, b) describe striking deviations from the pattern.
4). Relate measures of center and spread to the shape of the distribution, and choose the appropriate measures in different contexts.
5). Compare and contrast distributions (of quantitative data) from two or more groups, and produce a brief summary, interpreting your findings in context.
5). Apply the standard deviation rule to the special case of distributions having the "normal" shape.

his is a complete course in chemical stoichiometry, which is a set of tools chemists use to count molecules and determine the amounts of substances consumed and produced by reactions. The course is set in a scenario that shows how stoichiometry calculations are used in real-world situations. The list of topics (see below) is similar to that of a high school chemistry course, although with a greater focus on reactions occurring in solution and on the use of the ideas to design and carry out experiments. Topics covered include: Dimensional Analysis, the Mole, Empirical Formulas, Limiting Reagents, Titrations, Reactions Involving Mixtures.

You and a friend are hiking the Appalachian Trail when a storm comes through. You stop to eat, but find that all available firewood is too wet to start a fire. From your Chem 106 class, you remember that heat is given off by some chemical reactions; if you could mix two solutions together to produce an exothermic reaction, you might be able to cook the food you brought along for the hike. Luckily, being the dedicated chemist that you are, you never go anywhere without taking along a couple chemical solutions called X and Y just for times like this. The Virtual Lab contains solutions of compounds X and Y of various concentrations.

You probably remember the mole from high school chemistry, but do you remember why it is useful to chemists? The goal of the following video is to give the "big picture" of the mole and its applications; information on how to use the mole in calculations can be found in another tutorial. Throughout this course, we will use the term "molecular weight" to refer to the mass of a mole of a substance (for instance, the molecular weight of oxygen (O2) is 32 g/mol). Recent textbooks refer to this as "molar mass" to emphasize (i) that this term refers to the mass, not the weight, of substance, and (ii) that the quantity refers to a mole of a substance, not a single molecule. "Molecular weight" may be less precise, but it remains the term that most practicing chemists use in the laboratory. For this reason, we continue to use "molecular weight" in this course.

During the first kinetics lecture, we traced the efforts of atmospheric chemists to explain the depletion of ozone in the upper atmosphere. (The powerpoint slides have been posted on Blackboard for your review.) U2 spy planes gathered much of the initial data that linked ClO in the stratosphere to the ozone depletion. The data collected during these flights showed the concentrations of various chemical species in the stratosphere, but did not measure how fast the processes were occurring. To determine the kinetics (rates) of ozone depletion reactions, chemists perform controlled laboratory studies. In this homework, we will interpret data obtained from such laboratory experiments to study the ozone depletion reaction.

LEARNING OBJECTIVE: Identify and distinguish between a parameter and a statistic.

LEARNING OBJECTIVE: Explain the concepts of sampling variability and sampling distribution.

Explain how a density function is used to find probabilities involving continuous random variables.

Learning Objective: Apply probability rules in order to find the likelihood of an event.

Public policy issues are important to every field of engineering. Yet, most engineering students know little about the topic. For most students, however, an entire course focused on the topic is not necessary. For example, a class on engineering design could incorporate a case study on 3D printing policy.

This course will introduce students to the interrelationship of engineering and public policy, how to conduct neutral policy analysis, and then apply that knowledge in case studies to practice the skills they have learned. The modules takes a flipped classroom/active learning approach by using short videos to educate students, activities to practice the skills taught, and incorporates real-world examples such as hydraulic fracturing, drones, and 3D printing.

The STEM Readiness course provides a refresher of core skills related to STEM careers. The core skills covered are Mathematics from arithmetic to beginning algebra, Workplace Communications and Professionalism. The topics of the course are presented through workplace scenarios to show learners how these skills apply to their potential careers. In reviewing these core skills students will be better prepared to be successful in post-secondary STEM related technical programs and ultimately in STEM related careers.

1). Identify the sampling method used in a study and discuss its implications and potential limitations.
2). Critically evaluate the reliability and validity of results published in mainstream media.
3). Summarize and describe the distribution of a categorical variable in context.