This book is meant to be a textbook for a standard one-semester introductory statistics course for general education students.Over time the core content of this course has developed into a well-defined body of material that is substantial for a one-semester course. The authors believe that the 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 we have 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 our professional judgment and experience.

Introductory Statistics is intended for the one-semester introduction to statistics course for students who are not mathematics or engineering majors. It focuses on the interpretation of statistical results, especially in real world settings, and assumes that students have an understanding of intermediate algebra. In addition to end of section practice and homework sets, examples of each topic are explained step-by-step throughout the text and followed by a Try It problem that is designed as extra practice for students. This book also includes collaborative exercises and statistics labs designed to give students the opportunity to work together and explore key concepts. While the book has been built so that each chapter builds on the previous, it can be rearranged to accommodate any instructor’s particular needs.

In the 1920s, biologist and statistician Ronald Fisher met a woman who claimed she could taste whether a cup of tea was prepared by adding milk before or after the tea. Fisher's essay about the tea party may be one of the first cases published to teach the process of science. The present case is a modernized version of Fisher's story and concerns the ability of a woman to distinguish between cups of coffee with milk added first or second. The case is enhanced by a demonstration in which students distinguish between 2% and skim milk, brands of bottled water, or brands of soda.

This course presents a unique and challenging perspective on the causes of human disease and mortality. The course focuses on analyses of major causes of mortality in the US since 1900: cancer cardiovascular and cerebrovascular diseases, diabetes, infectious diseases. Students create analytical models to derive estimates for historically variant population risk factors and physiological rate parameters, and conduct analyses of familial data to separately estimate inherited and environmental risks. The course evaluates the basic population genetics of dominant, recessive and non-deleterious inherited risk factors.

In this 5-lesson unit, students engage in measurement activities involving length, area, volume, time, and weight, using objects, pictures and symbols. Students practice measuring using standard and nonstandard units. Some lessons are introduced using children's literature.

This three lesson unit from Illuminations focuses on connections between mathematics and football with the common theme of the Super Bowl. Students are asked to look at the Super Bowl not just as "the big game" but as an opportunity to apply mathematics to some interesting problems. The activities involve number sense, measurement, statistics, estimations, and problem solving.

This grades 6-8 activity focuses on interpreting and creating graphs that are functions of time. Two activity sheets focus on graphs of time vs speed; two others look at how many times an event occurred in a specific amount of time. Inventing stories to correspond to the graphs is challenging but fun!

Students experience data collection, analysis and inquiry in this LEGO® MINDSTORMS® NXT -based activity. They measure the position of an oscillating platform using a ultrasonic sensor and perform statistical analysis to determine the mean, mode, median, percent difference and percent error for the collected data.

Students learn about the statistical analysis of measurements and error propagation, reviewing concepts of precision, accuracy and error types. This is done through calculations related to the concept of density. Students work in teams to each measure the dimensions and mass of five identical cubes, compile the measurements into small data sets, calculate statistics including the mean and standard deviation of these measurements, and use the mean values of the measurements to calculate density of the cubes. Then they use this calculated density to determine the mass of a new object made of the same material. This is done by measuring the appropriate dimensions of the new object, calculating its volume, and then calculating its mass using the density value. Next, the mass of the new object is measured by each student group and the standard deviation of the measurements is calculated. Finally, students determine the accuracy of the calculated mass by comparing it to the measured mass, determining whether the difference in the measurements is more or less than the standard deviation.

This course serves as an introduction to working in an engineering laboratory. The student will learn to gather, analyze, interpret, and explain physical measurements for simple engineering systems in which only a few factors need be considered. Upon successful completion of this course, the student will be able to: Interpret and use scientific notation and engineering units to describe physical quantities; Present engineering data and other information in graphical and/or tabular format; Use automated systems for data acquisition and analysis for engineering systems; Work in teams for experiment design, data acquisition, and data analysis; Use elementary concepts of physics to analyze engineering situations and data; Summarize and present experimental design, implementation, and data in written format; Use new technology and resources to design and perform experiments for engineering analysis. (Mechanical Engineering 301)

In this exciting episode, DethWench shows how to merge confidential data from a cartoon clinic, and what to do with it afterward.

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This unit is the fourth in the MSXR209 series of five units on mathematical modelling. In this unit you will be taken through the whole modelling process in detail, from creating a first simple model, through evaluating it, to the subsequent revision of the model by changing one of the assumptions. The problem that will be examined is one based on heat transfer. This unit assumes you have studied Modelling pollution in the Great Lakes (MSXR209_1), Analysing skid marks (MSXR209_2) and Developing modelling skills (MSXR209_3).

This unit is the first in the MSXR209 series of five units that introduce the idea of modelling with mathematics. This unit centres on a mathematical model of how pollution levels in the Great Lakes of North America vary over a period of time. It demonstrates that, by keeping the model as simple as possible extremely complex systems can be understood and predicted.

This unit lays the foundation of the subject of mechanics. Mechanics is concerned with how and why objects stay put, and how and why they move. In particular, this unit - Modelling Static Problems - considers why objects stay put. And it assumes that you have a good working knowledge of vectors.

In this class, students use data and systems knowledge to build models of complex socio-technical systems for improved system design and decision-making. Students will enhance their model-building skills, through review and extension of functions of random variables, Poisson processes, and Markov processes; move from applied probability to statistics via Chi-squared t and f tests, derived as functions of random variables; and review classical statistics, hypothesis tests, regression, correlation and causation, simple data mining techniques, and Bayesian vs. classical statistics. A class project is required.

Natural Resources Biometrics begins with a review of descriptive statistics, estimation, and hypothesis testing. The following chapters cover one- and two-way analysis of variance (ANOVA), including multiple comparison methods and interaction assessment, with a strong emphasis on application and interpretation. Simple and multiple linear regressions in a natural resource setting are covered in the next chapters, focusing on correlation, model fitting, residual analysis, and confidence and prediction intervals. The final chapters cover growth and yield models, volume and biomass equations, site index curves, competition indices, importance values, and measures of species diversity, association, and community similarity.

" This class examines tools, data, and ideas related to past climate changes as seen in marine, ice core, and continental records. The most recent climate changes (mainly the past 500,000 years, ranging up to about 2 million years ago) will be emphasized. Quantitative tools for the examination of paleoceanographic data will be introduced (statistics, factor analysis, time series analysis, simple climatology)."

In Part 1, Monika demonstrates using Proc SQL to construct an array in SAS. In Part 2, DethWench demonstrates using that constructed array in SAS with the combination of a data step and Proc SQL code.

In this three-lesson unit students conduct surveys, create graphs, and explore combinations related to pizza toppings. Each lesson plan contains worksheets in PDF format.

This lesson introduces the law of large numbers. [Probability playlist: Lesson 29 of 29]