Students are presented with a real-life problem of flooding and erosion in the town of Simonton. They must use historical dischage data to determine the future risk of flooding. They must also use historical map data to asses the risk of future losses due to erosion. Using these data, they must dertermine the feasibility of levee systems proposed by the Corp of Engineers. Lastly, they must discuss their assumption and possible sources of error.

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The Open for Antiracism (OFAR) Program – co-led by CCCOER and College of the Canyons – emerged as a response to the growing awareness of structural racism in our educational systems and the realization that adoption of open educational resources (OER) and open pedagogy could be transformative at institutions seeking to improve. The program is designed to give participants a workshop experience where they can better understand anti-racist teaching and how the use of OER and open pedagogy can empower them to involve students in the co-creation of an anti-racist classroom. The capstone project involves developing an action plan for incorporating OER and open pedagogy into a course being taught in the spring semester. OFAR participants are invited to remix this template to design and share their projects and plans for moving this work forward. 

This material is a detailed exercise for students in introductory statistics. Students are asked to collect a random sample of data from a real estate website; conduct descriptive statistics (including confidence intervals); and write a report summarizing their findings.

This activity helps students develop a better understanding and stronger reasoning skills about the Central Limit Theorem and normal distributions.

Key words: Sample, Normal Distribution, Model, Distribution, Variability, Central Limit Theorem (CLT)

6.780 covers statistical modeling and the control of semiconductor fabrication processes and plants. Topics covered include: design of experiments, response surface modeling, and process optimization; defect and parametric yield modeling; process/device/circuit yield optimization; monitoring, diagnosis, and feedback control of equipment and processes; and analysis and scheduling of semiconductor manufacturing operations.

This activity has students use Fathom to test the correlation between attendance and ballpark capacity of major league baseball teams by taking a sample of actual data and scrambling one of the variables to see how the correlation behaves when the variables are not related. After displaying the distribution of correlations for many simulated samples, students find an approximate p-value based on the number of simulations that exceed the actual correlation.

A java applet that simulates the sampling distribution of the mean. It allows students to explore the effect of sample size.

Spreadsheets Across the Curriculum/Geology of National Parks module. Students use the geometric mean and multiplicative standard deviation to examine the right-skewed distribution of nutrient concentrations in water-quality data at Mammoth Cave National Park.

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 

Steps on how to calculate standard deviation of a sample using only a calculator - not with software.

Dawson (1995) presented a data set giving a population at risk and fatalities for an “unusual episode” (the sinking of the ocean liner Titanic) and discussed the use of the data set in a first statistics course as an elementary exercise in statistical thinking, the goal being to deduce the origin of the data. Simonoff (1997) discussed the use of this data set in a second statistics course to illustrate logistic regression. Moore (2000) used an abbreviated form of the data set in a chapter exercise on the chi-square test. This article describes an activity that illustrates contingency table (two-way table) analysis. Students use contingency tables to analyze the “unusual episode” data (from Dawson 1995) and attempt to use their analysis to deduce the origin of the data. The activity is appropriate for use in an introductory college statistics course or in a high school AP statistics course.

As teachers of statistics, we know that residual plots and other diagnostics are important to deciding whether or not linear regression is appropriate for a set of data. Despite talking with our students about this, many students might believe that if the correlation coefficient is strong enough, these diagnostic checks are not important. The data set included in this activity was created to lure students into a situation that looks on the surface to be appropriate for the use of linear regression but is instead based (loosely) on a quadratic function.

This article describes an interactive activity illustrating general properties of hypothesis testing and hypothesis tests for proportions. Students generate, collect, and analyze data. Through simulation, students explore hypothesis testing concepts. Concepts illustrated are: interpretation of p-values, type I error rate, type II error rate, power, and the relationship between type I and type II error rates and power. This activity is appropriate for use in an introductory college or high school statistics course.

This is a simulation for a beginning econometrics course that shows students how heteroskedasticity biases an estimator and why the power of a statistical text is important.

Students are introduced to the technology of flexible circuits, some applications and the photolithography fabrication process. They are challenged to determine if the fabrication process results in a change in the circuit dimensions since, as circuits get smaller and smaller (nano-circuits), this could become very problematic. The lesson prepares students to conduct the associated activity in which they perform statistical analysis (using Excel® and GeoGebra) to determine if the circuit dimension sizes before and after fabrication are in fact statistically different. A PowerPoint® presentation and post-quiz are provided. This lesson and its associated activity are suitable for use during the last six weeks of the AP Statistics course; see the topics and timing note for details.

Students apply pre-requisite statistics knowledge and concepts learned in an associated lesson to a real-world state-of-the-art research problem that asks them to quantitatively analyze the effectiveness of different cracked steel repair methods. As if they are civil engineers, students statistically analyze and compare 12 sets of experimental data from seven research centers around the world using measurements of central tendency, five-number summaries, box-and-whisker plots and bar graphs. The data consists of the results from carbon-fiber-reinforced polymer patched and unpatched cracked steel specimens tested under the same stress conditions. Based on their findings, students determine the most effective cracked steel repair method, create a report, and present their results, conclusions and recommended methods to the class as if they were presenting to the mayor and city council. This activity and its associated lesson are suitable for use during the last six weeks of the AP Statistics course; see the topics and timing note for details.

Working as if they are engineers aiming to analyze and then improve data collection devices for precision agriculture, students determine how accurate temperature sensors are by comparing them to each other. Teams record soil temperature data during a class period while making changes to the samples to mimic real-world crop conditions—such as the addition of water and heat and the removal of the heat. Groups analyze their collected data by finding the mean, median, mode, and standard deviation. Then, the class combines all the team data points in order to compare data collected from numerous devices and analyze the accuracy of their recording devices by finding the standard deviation of temperature readings at each minute. By averaging the standard deviations of each minute’s temperature reading, students determine the accuracy of their temperature sensors. Students present their findings and conclusions, including making recommendations for temperature sensor improvements.

This is a new approach to an introductory statistical inference textbook, motivated by probability theory as logic. It is targeted to the typical Statistics 101 college student, and covers the topics typically covered in the first semester of such a course. It is freely available under the Creative Commons License, and includes a software library in Python for making some of the calculations and visualizations easier.

This course is divided into two sections, Part I and Part II. Part I, found here, provides an introduction to statistical theory. Topics include normal distribution, limit theorems, Bayesian concepts, and testing, among others.

The authors of this book adapted homework problems to improve accessibility and promote diversity, equity, and inclusion in the introductory statistics course they teach at Fitchburg State University. The problems are showcased in this book, but we have also incorporated them into our existing problem sets on an open-source online homework platform called WeBWorK. The problems can be used as a companion to the OpenStax textbook "Introductory Statistics" by Barbara Illowsky and Susan Dean or any other textbook for a semester-long introductory statistics course. For a fuller experience for you and your students, we encourage you to contact us for help accessing the problem sets on WeBWorK. On that platform, students will engage more fully with the questions, and a slightly different version of the same problem will be generated for each student.