Search Results (9)
Elementary Statistics is an introduction to data analysis course that makes use of graphical and numerical techniques to study patterns and departures from patterns. The student studies randomness with emphasis on understanding variation, collects information in the face of uncertainty, checks distributional assumptions, tests hypotheses, uses probability as a tool for anticipating what the distribution of data may look like under a set of assumptions, and uses appropriate statistical models to draw conclusions from data. The course introduces the student to applications in engineering, business, economics, medicine, education, the sciences, and other related fields. The use of technology (computers or graphing calculators) will be required in certain applications.
Here's a "game" you can use with faculty, staff, and students to showcase the financial challenges that our students face in achieving their educational goals. You are welcome to customize the files for your own institution. We have "played" the game in Opening Day events, professional development events, and with student leaders. It never stops surprising players of the incredible challenges that we can assist with by eliminating textbook costs.
Intermediate Algebra Student Workbook, modified by De Anza College's Professor Doli Bambhania, is based upon Intermediate Algebra Student Workbook, Fourth Edition, 2014, by Scottsdale Community College. The included content is designed to lead students through Intermediate Algebra, from a functions modeling approach, and to develop a deep understanding of the concepts associated with functions, from mathematical, computational and applications perspectives. The workbook assumes the knowledge of prealgebra, as well as topics surrounding linear and quadratic functions. Access to a graphing calculator is assumed and the use of one is required for some of the lessons and practice problems.
Introductory Business Statistics is designed to meet the scope and sequence requirements of the one-semester statistics course for business, economics, and related majors. Core statistical concepts and skills have been augmented with practical business examples, scenarios, and exercises. The result is a meaningful understanding of the discipline, which will serve students in their business careers and real-world experiences.
This file contains an extended interview with Dr. Barbara Illowsky on her OER passion and advocacy. The interview was conducted as part of the Dual Enrollment & OER research report produced by Hanover Research.
This practice brief provides guidance to help K12 and community college dual enrollment program instructors and administrators identify and implement programs using open educational resources (OERs). This practice brief focuses on how OERs can remove potential barriers to students’ access to higher education by promoting textbook affordability for all students. As high schools typically cover the cost of textbooks and other course materials, the costs of college textbooks may deter high school students from considering dual enrollment programs, particularly low-income students. The integration of OERs in dual enrollment courses may help mitigate this barrier.
This collection of spreadsheet-based labs was funded as part of the Digital Learning Research Network (dLRN) made possible by a grant from the Bill and Melinda Gates Foundation. The labs were adapted from the Statistics book, “Introduction to Statistics,” published by OpenStax College. The original labs used graphing calculators and were found within the book after each chapter. These interactive spreadsheet-based labs are effective for online and face-face courses. They may also be used with the book (see Resource: Lab Mapping to Book Chapters) or stand-alone.Authors: Barbara Illowsky PhD, Foothill-De Anza Community College District; Larry Green PhD, Lake Tahoe Community College; James Sullivan, Sierra College; Lena Feinman,College of San Mateo; Cindy Moss, Skyline College; Sharon Bober, Pasadena Community College; Lenore Desilets, De Anza Community College.Lab Mapping to Book ChaptersGrading RubricLabsUnivariarate Data Normal DistributionCentral Limit TheoremHyporhesis Test - Single MeanHyporhesis Test - Single ProportionGoodness of FitLinear Regression
This module introduces the concepts of qualitative data, quantitative continuous data, and quantitative discrete data as used in statistics. Sample problems are included.