By the end of this section, you will be able to:Explain what a correlation coefficient tells us about the relationship between variablesRecognize that correlation does not indicate a cause-and-effect relationship between variablesDiscuss our tendency to look for relationships between variables that do not really existExplain random sampling and assignment of participants into experimental and control groupsDiscuss how experimenter or participant bias could affect the results of an experimentIdentify independent and dependent variables

Measuring the dimensions of nano-circuits requires an expensive, high-resolution microscope with integrated video camera and a computer with sophisticated imaging software, but in this activity, students measure nano-circuits using a typical classroom computer and (the free-to-download) GeoGebra geometry software. Inserting (provided) circuit pictures from a high-resolution microscope as backgrounds in GeoGebra's graphing window, students use the application's tools to measure lengths and widths of circuit elements. To simplify the conversion from the on-screen units to the real circuits' units and the manipulation of the pictures, a GeoGebra measuring interface is provided. Students export their data from GeoGebra to Microsoft® Excel® for graphing and analysis. They test the statistical significance of the difference in circuit dimensions, as well as obtain a correlation between average changes in original vs. printed circuits' widths. 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 below for details.

This course addresses the challenges of defining a relationship between exposure to environmental chemicals and human disease. Course topics include epidemiological approaches to understanding disease causation; biostatistical methods; evaluation of human exposure to chemicals, and their internal distribution, metabolism, reactions with cellular components, and biological effects; and qualitative and quantitative health risk assessment methods used in the U.S. as bases for regulatory decision-making. Throughout the term, students consider case studies of local and national interest.

How genetics can add to our understanding of cognition, language, emotion, personality, and behavior. Use of gene mapping to estimate risk factors for psychological disorders and variation in behavioral and personality traits. Mendelian genetics, genetic mapping techniques, and statistical analysis of large populations and their application to particular studies in behavioral genetics. Topics also include environmental influence on genetic programs, evolutionary genetics, and the larger scientific, social, ethical, and philosophical implications.

Defines what "Out of Range Values" are and how to account for them in data collection and statistical analysis.

This chart will help you identify which statistical analysis to used depending on your research question.How to use: When deciding which statistics to use, first you must ask what is your research question looking for (difference or association). Then, identify what type of variable are you dealing with (Nominal, Ordinal, or Interval / Ratio), then the number of independent variables (IV) or dependent variables (DV) depending on the nature of your hypothesis. Below is a chart to help you identify which analysis to use for your hypothesis testing. 

In this data rich world, we need to understand how things are organized on the Earth's surface. Those things are represented by spatial data and necessarily depend upon what surrounds them. Spatial statistics provide insights into explaining processes that create patterns in spatial data. In geographical information analysis, spatial statistics such as point pattern analysis, spatial autocorrelation, and spatial interpolation will analyze the spatial patterns, spatial processes, and spatial association that characterize spatial data. Understanding spatial analysis will help you realize what makes spatial data special and why spatial analysis reveals a truth about spatial data.

The goal of this assignment is for students to recognize that adding some randomization and "noise" to a model yields different results each time we run the model, and we can pull some useful statistics from these model results. This introduces the concept of a Monte Carlo method to the students.

The project asks students to explore the connection between racial discrimination and racial disparities in U.S. prison populations using credible sources of information including qualitative and statistical methods. The resource was developed by a faculty member and librarian in order to promote using library resources in the classroom. It was designed to encourage information literacy among students.  

9.63 teaches principles of experimental methods in human perception and cognition, including design and statistical analysis. The course combines lectures and hands-on experimental exercises and requires an independent experimental project. Some experience in programming is desirable. To foster improved writing and presentation skills in conducting and critiquing research in cognitive science, students are required to provide reports and give oral presentations of three team experiments. A fourth individually conducted experiment includes a proposal with revision, and concluding written and oral reports.

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.

This engineering design challenge is a great hands-on activity that utilizes the engineering design process, 3D modeling, and 3D printing technology. The challenge can be completed individually or in groups of 2 to 3. Students will work to complete the following challenge: Using the design process, design, document, model, and produce a toy car with interchangeable parts.

This engineering design challenge is a great hands-on activity that utilizes the engineering design process, 3D modeling, and 3D printing technology. The challenge can be completed individually or in groups of 2 to 3. Students will work to complete the following challenge: Using the design process, design, document, model, and produce a toy car with interchangeable parts.

This engineering design challenge is a great hands-on activity that utilizes the engineering design process, 3D modeling, and 3D printing technology. The challenge can be completed individually or in groups of 2 to 3. Students will work to complete the following challenge: Using the design process, design, document, model, and produce a toy car with interchangeable parts.

This engineering design challenge is a great hands-on activity that utilizes the engineering design process, 3D modeling, and 3D printing technology. The challenge can be completed individually or in groups of 2 to 3. Students will work to complete the following challenge: Using the design process, design, document, model, and produce a toy car with interchangeable parts.

This engineering design challenge is a great hands-on activity that utilizes the engineering design process, 3D modeling, and 3D printing technology. The challenge can be completed individually or in groups of 2 to 3. Students will work to complete the following challenge: Using the design process, design, document, model, and produce a toy car with interchangeable parts.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Microbiome sequencing data are very complex. In order to simplify analyses, researchers often perform unsupervised clustering to identify naturally occurring clusters and then investigate the clusters’ associations with various characteristics of interest. However, clustering performance and related conclusions can vary depending on the algorithm or beta diversity metric used. To improve microbiome analysis methods, a new study tested the performance of several metrics on four datasets with well-separated groups and a clinical dataset with less-clear group separation. None of the metrics was universally superior, but certain metrics underperformed under certain conditions. For example, the Bray-Curtis metric performed poorly in a dataset with rare high-abundance OTUs (groups of related bacteria), while the unweighted UniFrac metric performed poorly in a dataset with prevalent low-abundance OTUs..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

Students will learn how the U.S. Census Bureau helps emergency responders provide support during natural disasters. Then, the teacher will set up various stations around the room to encourage peer-to-peer learning in small groups. Students will rotate from station to station, completing tasks such as creating an emergency preparedness kit, determining the states with the highest risk for hurricanes, and reviewing a series of photos of houses to determine which are most likely to survive a natural disaster.

Psychology is designed to meet scope and sequence requirements for the single-semester introduction to psychology course. The book offers a comprehensive treatment of core concepts, grounded in both classic studies and current and emerging research. The text also includes coverage of the DSM-5 in examinations of psychological disorders. Psychology incorporates discussions that reflect the diversity within the discipline, as well as the diversity of cultures and communities across the globe.Senior Contributing AuthorsRose M. Spielman, Formerly of Quinnipiac UniversityContributing AuthorsKathryn Dumper, Bainbridge State CollegeWilliam Jenkins, Mercer UniversityArlene Lacombe, Saint Joseph's UniversityMarilyn Lovett, Livingstone CollegeMarion Perlmutter, University of Michigan