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.
This course offers an overview of healthcare, health information technology, and health information management systems. The focus is on the role and responsibilities of entry-level health IT specialists in each phase of the health information management systems lifecycle. The curriculum is aligned to the new Certified Associate in Healthcare Information and Management Systems (CAHIMS) certification administered by the Healthcare Information and Management Systems Society. This certificate is designed for students who have previous experience in IT or healthcare and it is designed to serve as a pathway into health IT careers.
This course is a collaborative effort of TAACCCT grantees, in particular Bellevue Colleges and MoHealthWINs. The collaborative partners supporting the development of this course are Open Learning Initiative (OLI) at Stanford University and Carnegie Mellon University, Center for Applied Science and Technology (CAST), and Creative Commons (CC)- all funded through the Gates foundation OPEN Grant. The core development team includes content experts, learning scientists, software developers, instructional designers, universal design for learning experts and instructional technologists.
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.
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.
This course is design to support the development of foundational skills in workplace communication and mathematics that are used in various STEM careers. The course offers practice using workplace communication and math skills that are encountered in the workforce. The activities are designed to strengthen skills in preparation for entering a college program in a STEM career.
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.