This 13-minute video lesson provides more discussion of the Central Limit Theorem and the Sampling Distribution of the Sample Mean. [Statistics playlist: Lesson 37 of 85]

This 9-minute video lesson provides a second regression example. [Statistics playlist: Lesson 69 of 85]

This 9-minute video lesson discusses the small sample hypothesis test. [Statistics playlist: Lesson 51 of 85]

This 11-minute video lesson shows how to construct small sample size confidence intervals using t-distributions. [Statistics playlist: Lesson 46 of 85]

This 7-minute video lesson provides an introduction to the idea that one can find a line that minimizes the squared distances to the points. [Statistics playlist: Lesson 62 of 85]

This 15-minute video lesson looks at the Standard Error of the Mean (a.k.a. the standard deviation of the sampling distribution of the sample mean!). [Statistics playlist: Lesson 38 of 85]

This 12-minute video lesson looks at the T-Statistic Confidence Interval (for small sample sizes). [Statistics playlist: Lesson 52 of 85]

This 3-minute video lesson discusses Type I Errors. [Statistics playlist: Lesson 50 of 85]

This 11-minute video lesson discusses variance of differences of random variables. [Statistics playlist: Lesson 54 of 85]

This 12-minute video lesson explains variance of a population. [Statistics playlist: Lesson 13 of 85]

This 7-minute video lesson compares Z-statistics and T-statistics. [Statistics playlist: Lesson 49 of 85]

This 8-minute video lesson takes some problems from CK12.org to provide Z-score practice. [Statistics playlist: Lesson 31 of 85]

A project subject that teaches students how to create, carry out, interpret, and analyze a market research questionnaire. Emphasis on discovering market structure and segmentation, but students can pursue other project applications. Includes a user-oriented treatment of multivariate analysis (factor analysis, multidimensional scaling, conjoint and cluster analysis).

In order to promote students’ conceptual understanding and learning experience in introductory statistics, a technology task, which focuses on the probability distribution in which means are defined, was created using TinkerPlots, an exploratory data analysis and modeling software. The targeted audiences range from senior high school grade levels to college freshmen who are starting their introductory course in statistics. Students will be guided to explore and discover the movement behaviors of means of a set of numbers randomly generated from a fixed range of values characterized by a predetermined probability distribution. The cognitive, mathematical, technological and pedagogical natures of the task, as well as its association with the statistics education framework based on the Guidelines for Assessment and Instruction in Statistics Education (GAISE) by the American Statistical Association, will be elaborated. A brief discussion on what cognitive design principles this task satisfies will also be provided at the end.

Think Stats is an introduction to Probability and Statistics for Python programmers.

*Think Stats emphasizes simple techniques you can use to explore real data sets and answer interesting questions. The book presents a case study using data from the National Institutes of Health. Readers are encouraged to work on a project with real datasets.
*If you have basic skills in Python, you can use them to learn concepts in probability and statistics. Think Stats is based on a Python library for probability distributions (PMFs and CDFs). Many of the exercises use short programs to run experiments and help readers develop understanding.

When we come across a graph, a chart, or an infographic, how do we know if it is telling the truth? Often, numbers and data convey an authority that is hard to dispute, especially when they are arranged visually in a compelling way. Yet, data, in the ways it is gathered and shared, can be misrepresented and portray a slanted reality rather than a more accurate depiction.

This workshop introduces the concept of data literacy, or the ability to comprehend and interpret data, as a method of cultivating a critical mindset towards representations of information. Participants will learn how to discern misleading data visualizations and collaborate with others in developing strategies for analyzing data that is more accurate and useful. All consumers of information, especially undergraduate students, are encouraged to attend.

By the end of this workshop, participants will be able to:

* Understand what data is and how it is used for rhetorical, commercial, and political purposes

* Identify errors and discrepancies in how data is produced and represented

* Engage with data visualizations in multiple contexts with a healthy degree of skepticism

* Communicate their interpretations of data with their peers

This course provides an introduction to probability and statistics, with emphasis on engineering applications. Course topics include events and their probability, the Total Probability and Bayes' Theorems, discrete and continuous random variables and vectors, uncertainty propagation and conditional analysis. Second-moment representation of uncertainty, random sampling, estimation of distribution parameters (method of moments, maximum likelihood, Bayesian estimation), and simple and multiple linear regression. Concepts illustrated with examples from various areas of engineering and everyday life.