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.

Submitted as part of the California Learning Resource Network (CLRN) Phase 3 Digital Textbook Initiative (CA DTI3), CK-12 Advanced Probability and Statistics introduces students to basic topics in statistics and probability but finishes with the rigorous topics an advanced placement course requires. Includes visualizations of data, introduction to probability, discrete probability distribution, normal distribution, planning and conducting a study, sampling distributions, hypothesis testing, regression and correlation, Chi-Square, analysis of variance, and non-parametric statistics.

This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environmental Applications (1.017) and Uncertainty in Engineering (1.010), are being jointly offered and taught as a single course.

These are course lecture slides that are a companion for teaching ROTEL Project Statistics Through an Equity Lens [Revised Edition]. Statistics Through an Equity Lens [Revised Edition] carries a significant responsibility by presenting statistics through an equity lens. There are 8 chapters in Statistics Through an Equity Lens [Revised Edition]. Chapters 7 and 8 are Case Studies and Hypothesis Testing. This Ancillary Resource is Course Lecture Slides for Chapters 1-6. A brief outline of the chapters’ contents follows by section.  

This activity helps a student recognize the consequences of Type I and Type II errors in hypothesis testing.

This website is an interactive educational application developed to simulate and visualize various statistical concepts:

Law of Large Numbers
Central Limit Theorem
Confidence Intervals
Hypothesis Testing
ANOVA
Joint Distributions
Least Squares
Sample Distribution of OLS Estimators
The OLS Estimators are Consistent
Omitted Variable Bias
Multiple Regression

Project of Professor Tanya Byker and Professor Amanda Gregg at Middlebury College, with research assistants Kevin Serrao, Class of 2018, Dylan Mortimer, Class of 2019, Ammar Almahdy, Class of 2020, Jacqueline Palacios, Class of 2020, Siyuan Niu, Class of 2021, David Gikoshvili, Class of 2021, and Ethan Saxenian, Class of 2022.

In this activity, students perform the Kaplan-Meier survival analysis on raw data obtained from an experiment that explores the effect of overexpressing the Snakeskin (Ssk) protein on the lifespan of a population of fruit flies. This tight junction equivalent protein is expressed at higher levels than normal specifically in the gut and throughout the entire lifespan of the flies. Because aging guts are accompanied by both a mislocalization and a decrease in the expression levels of junctional proteins, including Ssk, it was hypothesized that overexpressing Ssk may lead to a healthier fly with a longer lifespan. This data will be analyzed in order to determine if there is a significant difference between the control flies in which no overexpression occurs and the experimental group in which Ssk is overexpressed.

This open course for Elementary Statistics was created through a Round Ten Textbook Transformation Grant:

https://oer.galileo.usg.edu/mathematics-collections/39/

The open course contains ancillary materials for OpenStax Introductory Statistics:

https://openstax.org/details/books/introductory-statistics

Included in the course are introductions to each lesson, lecture slides, videos, and problem questions. Topics include:

Types of Data
Sampling Techniques
Qualitative Data
Frequency Distributions
Descriptive Statistics
Variation and Position
Confidence Intervals
Hypothesis Testing
Chi-Square Goodness of Fit
Linear Regression
Variance ANOVA

Preclinical studies using animals to study the potential of a therapeutic drug or strategy are important steps before translation to clinical trials. However, evidence has shown that poor quality in the design and conduct of these studies has not only impeded clinical translation but also led to significant waste of valuable research resources. It is clear that experimental biases are related to the poor quality seen with preclinical studies. In this chapter, we will focus on hypothesis testing type of preclinical studies and explain general concepts and principles in relation to the design of in vivo experiments, provide definitions of experimental biases and how to avoid them, and discuss major sources contributing to experimental biases and how to mitigate these sources. We will also explore the differences between confirmatory and exploratory studies, and discuss available guidelines on preclinical studies and how to use them. This chapter, together with relevant information in other chapters in the handbook, provides a powerful tool to enhance scientific rigour for preclinical studies without restricting creativity.

Students are introduced to concepts of hypothesis testing using elementary hypothesis tests, t-tests, and p-values as they compare a given fish population for methylmercury levels (using real and hypothetical data) against real-world mercury standards.

Students are introduced to concepts of sampling distributions and hypothesis testing using a simulation applet, elementary hypothesis tests, t-tests, and p-values as they compare a given fish population for methylmercury levels (using real and hypothetical data) against real-world mercury standards.

This Statistics resource was developed under the guidance and support of experienced high school teachers and subject matter experts. It is presented here in multiple formats: PDF, online, and low-cost print. Statistics offers instruction in grade-level appropriate concepts and skills in a logical, engaging progression that begins with sampling and data and covers topics such as probability, random variables, the normal distribution, and hypothesis testing. This content was developed with students in mind, incorporating statistics labs, worked exercises, and additional opportunities for assessment that incorporate real-world statistical applications. For instructors, resources are available to support the implementation of the Statistics textbook, including a Getting Started Guide, direct instruction presentations, and a solutions manual.

This course is an introduction to data cleaning, analysis and visualization. We will teach the basics of data analysis through concrete examples. You will learn how to take raw data, extract meaningful information, use statistical tools, and make visualizations.
This was offered as a non-credit course during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month.

This course examines signals, systems and inference as unifying themes in communication, control and signal processing. Topics include input-output and state-space models of linear systems driven by deterministic and random signals; time- and transform-domain representations in discrete and continuous time; group delay; state feedback and observers; probabilistic models; stochastic processes, correlation functions, power spectra, spectral factorization; least-mean square error estimation; Wiener filtering; hypothesis testing; detection; matched filters.

This course is a self-contained introduction to statistics with economic applications. Elements of probability theory, sampling theory, statistical estimation, regression analysis, and hypothesis testing. It uses elementary econometrics and other applications of statistical tools to economic data. It also provides a solid foundation in probability and statistics for economists and other social scientists. We will emphasize topics needed in the further study of econometrics and provide basic preparation for 14.32 Econometrics. No prior preparation in probability and statistics is required, but familiarity with basic algebra and calculus is assumed.

This course will provide a solid foundation in probability and statistics for economists and other social scientists. We will emphasize topics needed for further study of econometrics and provide basic preparation for 14.32 Econometrics. Topics include elements of probability theory, sampling theory, statistical estimation, and hypothesis testing.

Introduction to Statistics is a resource for learning and teaching introductory statistics. This work is in the public domain. Therefore, it can be copied and reproduced without limitation. However, we would appreciate a citation where possible. Please cite as: Online Statistics Education: A Multimedia Course of Study (http://onlinestatbook.com/). Project Leader: David M. Lane, Rice University. Instructor's manual, PowerPoint Slides, and additional questions are available.

"Introductory Business Statistics with Interactive Spreadsheets - 1st Canadian Edition" is an adaptation of Thomas K. Tiemann's book, "Introductory Business Statistics". In addition to covering basics such as populations, samples, the difference between data and information, and sampling distributions, descriptive statistics and frequency distributions, normal and t-distributions, hypothesis testing, t-tests, f-tests, analysis of variance, non-parametric tests, and regression basics, the following information has been added: the chi-square test and categorical variables, null and alternative hypotheses for the test of independence, simple linear regression model, least squares method, coefficient of determination, confidence interval for the average of the dependent variable, and prediction interval for a specific value of the dependent variable. This new edition also allows readers to learn the basic and most commonly applied statistical techniques in business in an interactive way -- when using the web version -- through interactive Excel spreadsheets. All information has been revised to reflect Canadian content.

"An Intuitive, Interactive Introduction to Biostatistics" is an introductory statistics textbook oriented towards towards undergraduate students in the health sciences. While covering the breadth of material typically presented in a first semester statistics course, including introductions to probability and distributions, study design, CLT, hypothesis testing, and inference, IIIB distinguishes itself with its focus on cultivating student intuition through the use of guided questions and interactive simulation-based applets. Written in R, this open-source text has been created with customizability in mind, offering instructors maximal flexibility in arranging and modifying the content.

This textbook covers the contents of an introductory statistics class, as typically taught to undergraduate psychology, health or social science students. The book covers how to get started in jamovi as well as giving an introduction to data manipulation. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, correlation, t-tests, regression, ANOVA and factor analysis. Bayesian statistics are touched on at the end of the book.