All resources in Middlesex Community College

Workshop to Explore Cultural

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In this workshop participants will be able to discuss cultural sensitivity and learn to embrace diversity. Cultural blindness — being “fair” by treating everyone the same is often hard to view as problematic. Discussion in this workshop will focus on inspiring students to understand different cultures and beliefs as well as the importance of culturally sensitivity to these different beliefs. This workshop will encourage participants to; explore their own biases, consider different points of view and will utilize cultural lenses to develop cultural sensitivity.

Material Type: Activity/Lab, Assessment, Diagram/Illustration, Lesson, Reading

Author: Jennifer A Burns, MA

Current use of Technology in Teaching Introductory Statistics by Igor Baryakhtar

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Igor Baryakhtar's Presentation at Virtual NEMATYC 2021 Conference, 4/08/2021.The technologies that support learning of introductory statistics are reviewed. Advantages and disadvantages of using Graphing calculator TI 83 / TI 84, StatCrunch (Pearson's web- based statistical software), Apple Numbers, Microsoft Excel, R language and software is discussed. Tablet implementation of Introductory Statistic Open Education Resources based course is described.

Material Type: Teaching/Learning Strategy

Author: Igor Baryakhtar

Composition II: An Exploration of Reading and Writing

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This collection of resources covers the fundamentals of literature and encourages critical and thoughtful responses to a variety of writings, from short stories, poetry, and music to case studies and academic essays. There is a comprehensive guide to the basic building blocks of writing, with terms, discussion points, video examples, and pop-culture relevancy. A link to each writing is included, with works ranging from Sophocles to Bono. 

Material Type: Primary Source, Reading, Student Guide, Teaching/Learning Strategy, Textbook

Author: Stephanie Pesce

Introductory Statistics

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  Introductory Statistics is a non-calculus based, descriptive statistics course with applications. Topics include methods of collecting, organizing, and interpreting data; measures of central tendency, position, and variability for grouped and ungrouped data; frequency distributions and their graphical representations; introduction to probability theory, standard normal distribution, and areas under the curve. Course materials created by Fahmil Shah, content added to OER Commons by Victoria Vidal.

Material Type: Module

Author: Victoria Vidal

Introduction to Concepts in Statistics

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After the completion of this module, the student will be able to *read scatter plots and bar graphs *identify error bars on a graph *explain the role of a trend line *produce simple graphs in Excel The curricular materials contain a workbook (pdf and docx formats) and spreadsheets to work on the data and generate graphs.

Material Type: Activity/Lab, Homework/Assignment

Author: Claudia Neuhauser

Statistics for Laboratory Scientists I

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This course introduces the basic concepts and methods of statistics with applications in the experimental biological sciences. Demonstrates methods of exploring, organizing, and presenting data, and introduces the fundamentals of probability. Presents the foundations of statistical inference, including the concepts of parameters and estimates and the use of the likelihood function, confidence intervals, and hypothesis tests. Topics include experimental design, linear regression, the analysis of two-way tables, sample size and power calculations, and a selection of the following: permutation tests, the bootstrap, survival analysis, longitudinal data analysis, nonlinear regression, and logistic regression. Introduces and employs the freely-available statistical software, R, to explore and analyze data.

Material Type: Full Course, Homework/Assignment, Lecture Notes, Syllabus

Author: Broman, Karl

Statistics for Laboratory Scientists II

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This course introduces the basic concepts and methods of statistics with applications in the experimental biological sciences. Demonstrates methods of exploring, organizing, and presenting data, and introduces the fundamentals of probability. Presents the foundations of statistical inference, including the concepts of parameters and estimates and the use of the likelihood function, confidence intervals, and hypothesis tests. Topics include experimental design, linear regression, the analysis of two-way tables, sample size and power calculations, and a selection of the following: permutation tests, the bootstrap, survival analysis, longitudinal data analysis, nonlinear regression, and logistic regression. Introduces and employs the freely-available statistical software, R, to explore and analyze data.

Material Type: Full Course, Lecture Notes, Syllabus

Author: Broman, Karl

Introduction to Statistics (MATH 146)

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The main goal of the course is to highlight the general assumptions and methods that underlie all statistical analysis. The purpose is to get a good understanding of the scope, and the limitations of these methods. We also want to learn as much as possible about the assumptions behind the most common methods, in order to evaluate if they apply with reasonable accuracy to a given situation. Our goal is not so much learning bread and butter techniques: these are pre-programmed in widely available and used software, so much so that a mechanical acquisition of these techniques could be quickly done "on the job". What is more challenging is the evaluation of what the results of a statistical procedure really mean, how reliable they are in given circumstances, and what their limitations are.Login: guest_oclPassword: ocl

Material Type: Full Course, Homework/Assignment, Lecture Notes, Lesson Plan, Syllabus

Applied Statistics, Spring 2009

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I designed the course for graduate students who use statistics in their research, plan to use statistics, or need to interpret statistical analyses performed by others. The primary audience are graduate students in the environmental sciences, but the course should benefit just about anyone who is in graduate school in the natural sciences. The course is not designed for those who want a simple overview of statistics; we’ll learn by analyzing real data. This course or equivalent is required for UMB Biology and EEOS Ph.D. students. It is a recommended course for several of the intercampus graduate school of marine science program options.

Material Type: Full Course

Author: Eugene Gallagher

Methods in Biostatistics II

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Presents fundamental concepts in applied probability, exploratory data analysis, and statistical inference, focusing on probability and analysis of one and two samples. Topics include discrete and continuous probability models; expectation and variance; central limit theorem; inference, including hypothesis testing and confidence for means, proportions, and counts; maximum likelihood estimation; sample size determinations; elementary non-parametric methods; graphical displays; and data transformations.

Material Type: Full Course, Lecture Notes, Syllabus

Author: Brian Caffo