How can we conduct scientific research so that we have evidence to support a claim?Students in this problem-based learning module are invited to design a testable question to guide Scientific Research, Evaluate the pH of various solutions, Identify Variables, Conduct a Scientific Investigation, and Analyze/Communicate results.    How can we conduct scientific research so that we have evidence to support a claim? Antacid tablets are a multi-billion dollar industry.  Claims are made regularly by certain brands that their extra strength tablets contain “DOUBLE the acid neutralizing power per tablet of regular strength antacids.”  How effective are antacids?  Are double-strength antacids twice as effective as regular strength antacids?  Have you ever noticed a parent/guardian/family member take an antacid tablet? Stomach chemistry is about acids and bases.  When the pH of a stomach is too acidic then it might make the person have a stomach ache.  In some cases “heartburn” or “acid reflux” are used as terms to describe the problems some people face.  Antacids are usually basic which, when taken, might help raise the pH level in a stomach thus making a person feel better.You are invited to design an investigation with a partner, or a team of 4 students, to test your own idea about the effectiveness of antacids.  The challenge?  Have a driving question, clear variable identification, and an analysis of your results.  Materials for your test will be provided to you by your teacher.  At the culmination of your investigation your design team will make a 30-second pitch on your phone to show at your family Thanksgiving meal to explain the benefits (or negatives) of using antacids, and how antacids work.

An introduction and examples of how to use Correlation and Simple Linear Regression. Explaining concepts as coefficient of correlation, dependent variables, independent variables and the straight line equation and residuals.

6.0002 is the continuation of 6.0001 Introduction to Computer Science and Programming in Python and is intended for students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems and to help students, regardless of their major, feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class uses the Python 3.5 programming language.

Introduction to Sociology 2e adheres to the scope and sequence of a typical, one-semester introductory sociology course. It offers comprehensive coverage of core concepts, foundational scholars, and emerging theories, which are supported by a wealth of engaging learning materials. The textbook presents detailed section reviews with rich questions, discussions that help students apply their knowledge, and features that draw learners into the discipline in meaningful ways. The second edition retains the book’s conceptual organization, aligning to most courses, and has been significantly updated to reflect the latest research and provide examples most relevant to today’s students. In order to help instructors transition to the revised version, the 2e changes are described within the preface.

Define and describe the scientific methodExplain how the scientific method is used in sociological researchUnderstand the function and importance of an interpretive frameworkDefine what reliability and validity mean in a research study

Students typically find linear regression analysis of data sets in a biology classroom challenging. These activities could be used in a Biology, Chemistry, Mathematics, or Statistics course. The collection provides student activity files with Excel instructions and Instructor Activity files with Excel instructions and solutions to problems.

Students will be able to perform linear regression analysis, find correlation coefficient, create a scatter plot and find the r-square using MS Excel 365. Students will be able to interpret data sets, describe the relationship between biological variables, and predict the value of an output variable based on the input of an predictor variable.