This course is based on the 2018 AP Statistics curriculum and introduces everything from basic descriptive statistics to data collection to hot topics in data analysis like Big Data and neural networks.

Today we’re going to talk about how we compare things that aren’t exactly the same – or aren’t measured in the same way.

Last week we introduced the ANOVA model which allows us to compare measurements of more than two groups, and today we’re going to show you how it can be applied to look at data that belong to multiple groups that overlap and interact.

In this lesson, students explore how cable pricing has changed over time, both in terms of total dollars and the cost per channel.

In this lesson, students use scatterplots and linear regression to examine several variables that may help predict Olympic performance.

In this lesson, students write a quadratic function to model the number of possible connections between users on a social network.

Today we’re going to finish up our unit on data visualization by taking a closer look at how dot plots, box plots, and stem and leaf plots represent data.

Today we’re going to talk about confidence intervals.

Today we’re going to wrap up our discussion of General Linear Models (or GLMs) by taking a closer looking at two final common models: ANCOVA (Analysis of Covariance) and RMA (Repeated Measures ANOVA).

In this lesson, students use percents and probabilities to explore whether the show is legit, or whether there might be something shady going on!

In this lesson, students use quadratic functions to develop a model of expected points, which measure how many points a team can expect to score from different field positions.

In this lesson, students analyze almost thirty years’ worth of data summarized in n-way frequency tables and discuss whether they see evidence of racial bias in who receives the death penalty and who doesn’t.