# Activities

This document tells you how to grab a Word-formatted version of each activity or a web link to a plain HTML document containing the activity.

## Instructor Guide: Data basics

NOT YET AVAILABLE

## Jittering

Jittering is a graphical technique that extends point plots to make them more informative about categorical variables.

## Types of variables

Introduces the distinction between quantitative and categorical variables, the *range* of a quantitative variable and the *levels* of a categorical variable.

## Variables and the unit of observation

Gets students to think about what is the unit of observation in each data set and how to look up the meaning of each variable.

## Response and explanatory variables

Reasons to identify one variable as the response and another as the explanatory.

## Variation and the 95% summary interval

We typically describe variation or uncertainty using an *interval*. The 95% summary interval is a way to describe the spread of a variable.

## Experiment and causality

Causality as a reason to identify one variable as the response and another as the explanatory.

## The standard deviation and the 95% summary interval

Relating the 95% summary interval to a traditional measure of spread: the standard deviation

## Intervention and prediction

Causality as a reason to identify one variable as the response and another as the explanatory.

## Common, uncommon, and rare

Using the z-score to express everyday concepts of common and rare.

## Introducing linear regression

Discussion topics to introduce linear regression to your class.

## Shapes of distributions

Introduces terms such as skew, bi-modal, and flat, by reference to the difference of the actual variable from a theoretical normal distribution.

## Parameters of the normal distribution

Normal distributions are a *family*. The specific members of the family are identified by two parameters: the mean and the standard deviation.

## What is a confidence interval?

Describes the desired behavior of a confidence interval, that is, how to test whether a procedure produces a valid confidence interval.

## Sampling bias and the confidence interval

A confidence interval will cover the population parameter with the right frequency only if the sample is unbiased. This lesson explores sampling bias.

## Comparing two groups

Visualizing sampling variation in the difference between two groups.

## Comparing two confidence intervals

Using plain confidence intervals on the mean to decide if there's good evidence that the means are different.

## The two-sample t test

Having already looked at the overlap of confidence intervals on the means of two groups, formalizing the process as a t-test.

## Describing relationship patterns in words and numbers

Translating a regression line into a description in everyday terms.

## How much is explained?

Using R-squared to quantify how much of the variation in a response variable is accounted for by explanatory variables.

## Data and Point Plots

Introduces the distinction between quantitative and categorical variables through their very different appearances in a point plot.

## Bootstrapping Activity

NOT YET AVAILABLE. Finding a confidence interval by repeatedly sampling from the sample.

## Flexibility

NOT YET AVAILABLE. Determine whether a curved or straight-line relationship is a better description of the relationship between two quantitative variables.

## Changes in risk

NOT YET AVAILABLE. Risk is not a yes/no proposition.