This collection contains the California Open Educational Resources Council (CA OER) list of peer-reviewed textbooks for Introduction to Statistics (common course-id MATH 110), one of the top 50 college courses identified for inclusion in the California Open Online Library for Education (COOLforEd).
The use of probability techniques, hypothesis testing, and predictive techniques to facilitate decision-making. Topics include descriptive statistics; probability and sampling distributions; statistical inference; correlation and linear regression; analysis of variance, chi-square and t-tests; and application of technology for statistical analysis including the interpretation of the relevance of the statistical findings. Applications using data from disciplines including business, social sciences, psychology, life science, health science, and education.
Minimum Units: 3.0
Any rationale or comment:Typically satisfies general education quantitative reasoning requirement (CSU GE B4).
Select to open Course eportfolio pedagogical approach and outcomes
Pedagogical Approach & Learning Outcomes
Summarizing data graphically and numerically;
Descriptive statistics: measures of central tendency, variation, relative position, and levels/scales of measurement;
Sample spaces and probability;
Random variables and expected value;
Sampling and sampling distributions;
Discrete distributions – Binomial;
Continuous distributions – Normal;
The Central Limit Theorem;
Estimation and confidence intervals;
Hypothesis Testing and inference, including t-tests for one and two populations, and Chi-square test;
Correlation and linear regression and analysis of variance (ANOVA);
Applications using data from disciplines including business, social sciences, psychology, life science, health science, and education; and
Statistical analysis using technology such as SPSS, EXCEL, Minitab, or graphing calculators.
Upon successful completion of the course, students will be able to:
Distinguish among different scales of measurement and their implications;
Interpret data displayed in tables and graphically;
Apply concepts of sample space and probability;
Calculate measures of central tendency and variation for a given data set;
Identify the standard methods of obtaining data and identify advantages and disadvantages of each;
Calculate the mean and variance of a discrete distribution;
Calculate probabilities using normal and student’s t-distributions;
Distinguish the difference between sample and population distributions and analyze the role played by the Central Limit Theorem;
Construct and interpret confidence intervals;
Determine and interpret levels of statistical significance including p-values;
Interpret the output of a technology-based statistical analysis;
Identify the basic concept of hypothesis testing including Type I and II errors;
Formulate hypothesis tests involving samples from one and two populations;
Select the appropriate technique for testing a hypothesis and interpret the result;
Use linear regression and ANOVA analysis for estimation and inference, and interpret the associated statistics; and
Use appropriate statistical techniques to analyze and interpret applications based on data from disciplines including business, social sciences, psychology, life science, health science, and education.
Select to open Course eportfolio assessment methodology and other information
Assessment & Other Info
Homework or projects where students demonstrate their mastery of the learning objectives and their ability to devise, organize and present complete solutions to problems.
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The Shafer and Zhang open textbooks is designed as a one-semester statistics course for...
The Shafer and Zhang open textbooks is designed as a one-semester statistics course for general education students. The authors believe that students in this course are best served by a focus on the core material and not by an exposure to a plethora of peripheral topics. Therefore in writing this book they sought to present material that comprises fully a central body of knowledge that is defined according to convention, realistic expectation with respect to course duration and students’ maturity level, and their professional judgment and experience.
Each chaper features learning objectives, definitions, key takeaways, exercises, and answers to the exercises.
The Illowsky and Dean one-semester open textbook is geared towards students not majoring...
The Illowsky and Dean one-semester open textbook is geared towards students not majoring in math and science and focuses on the applications of statistical knowledge rather than the theory behind it. Based on the widely adopted Collaborative Statistics by the same authors, this new version has enhancements in artwork, terminology, and practical applications to enhance the relevance and accessibility for students. The text also includes collaborative exercises, integration with TI-83,83+,84+ calculators, technology integration problems, and statistics labs.
Dr. Barbara Illowsky's open textbook adoption for Elementary Statistics and Probability at De Anza College
Dr. Larry Green's open textbook adoption for Elementary Statistics at Lake Tahoe Community College
Dr. Irene Duranczyk's open textbook adoption for Statistics: Understanding and Applying Data at University of Minnesota
Introductory Statistics follows the scope and sequence of a one-semester, introduction to...
Introductory Statistics follows the scope and sequence of a one-semester, introduction to statistics course and is geared toward students majoring in fields other than math or engineering. This text assumes students have been exposed to intermediate algebra, and it focuses on the applications of statistical knowledge rather than the theory behind it. The foundation of this textbook is Collaborative Statistics, by Barbara Illowsky and Susan Dean, which has been widely adopted. Introductory Statistics includes innovations in art, terminology, and practical applications, all with a goal of increasing relevance and accessibility for students. We strove to make the discipline meaningful and memorable, so that students can draw a working knowledge from it that will enrich their future studies and help them make sense of the world around them. The text also includes Collaborative Exercises, integration with TI-83,83+,84+ Calculators, technology integration problems, and statistics labs.
Developed by Rice University, University of Houston at Clear Lake, and Tufts University,...
Developed by Rice University, University of Houston at Clear Lake, and Tufts University, this open textbook is a public domain resource for learning and teaching introductory statistics. It contains material presented in textbook format and as video presentations. This resource features interactive demonstrations and simulations, case studies, and an analysis lab.
We hope readers will take away three ideas from this book in addition to forming a...
We hope readers will take away three ideas from this book in addition to forming a foundation of statistical thinking and methods. (1) Statistics is an applied field with a wide range of practical applications. (2) You don’t have to be a math guru to learn from real, interesting data. (3) Data are messy, and statistical tools are imperfect. But, when you understand the strengths and weaknesses of these tools, you can use them to learn about the real world.