Given theoretical probabilities based on a chance experiment, students describe what they expect to see when they observe many outcomes of the experiment. Students distinguish between theoretical probabilities and estimated probabilities. Students understand that probabilities can be estimated based on observing outcomes of a chance experiment.

In this lesson, students understand that a probability is a number between 0 and 1 that represents the likelihood that an event will occur. Students interpret a probability as the proportion of the time that an event occurs when a chance experiment is repeated many times.

In this lesson, students use data from a random sample to estimate a population mean. Students understand the term "sampling variability" in the context of estimating a population mean.

In this lesson, students use data from a random sample to estimate a population mean. Students know that increasing the sample size decreases the sampling variability of the sample mean.

Given a description of a chance experiment that can be thought of as being performed in two or more stages, students use tree diagrams to organize and represent the outcomes in the sample space. Students calculate probabilities of compound events.

In this lesson, students recognize and justify that a linear model can be used to fit data. Students interpret the slope of a linear model to answer questions or to solve a problem.

In this lesson, students identify situations where it is reasonable to use a linear function to model the relationship between two numerical variables. Students interpret slope and the initial value in a data context.

In this lesson, students use row relative frequencies or column relative frequencies to informally determine if there is an association between two categorical variables.