This long (26 minute) presentation shows how to use an Excel spreadsheet to show that the normal distribution approximates the binomial distribution for a large number of trials. [Statistics playlist: Lesson 28 of 85]

This 7-minute video lesson discusses one-tailed and two-tailed tests. [Statistics playlist: Lesson 48 of 85]

This 10-minute video lesson looks at the proof (part 2) of minimizing the squared error to regression line. [Statistics playlist: Lesson 64 of 85]

This 13-minute video lesson looks at R-squared or the coefficient of determination. [Statistics playlist: Lesson 68 of 85]

This 2-minute video lesson shows how to find the range and the mid-range of a set of numbers. [Statistics playlist: Lesson 2 of 85]

This 3-minute video lesson explains how to read box and whisker plots. [Statistics playlist: Lesson 10 of 85]

This 9-minute video lesson give a Rregression line example. [Statistics playlist: Lesson 67 of 85]

This 14-minute video lesson shows how to figure out the probability of running out of water on a camping trip. [Statistics playlist: Lesson 39 of 85]

This 11-minute video lesson looks at the the central limit theorem and the sampling distribution of the sample mean. [Statistics playlist: Lesson 36 of 85]

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 course introduces the basic concepts and methods of statistics with applications in the experimental biological sciences. Demonstrates methods of exploring, organizing, and presenting data, and introduces the fundamentals of probability. Presents the foundations of statistical inference, including the concepts of parameters and estimates and the use of the likelihood function, confidence intervals, and hypothesis tests. Topics include experimental design, linear regression, the analysis of two-way tables, sample size and power calculations, and a selection of the following: permutation tests, the bootstrap, survival analysis, longitudinal data analysis, nonlinear regression, and logistic regression. Introduces and employs the freely-available statistical software, R, to explore and analyze data.

This course introduces the basic concepts and methods of statistics with applications in the experimental biological sciences. Demonstrates methods of exploring, organizing, and presenting data, and introduces the fundamentals of probability. Presents the foundations of statistical inference, including the concepts of parameters and estimates and the use of the likelihood function, confidence intervals, and hypothesis tests. Topics include experimental design, linear regression, the analysis of two-way tables, sample size and power calculations, and a selection of the following: permutation tests, the bootstrap, survival analysis, longitudinal data analysis, nonlinear regression, and logistic regression. Introduces and employs the freely-available statistical software, R, to explore and analyze data.