Chapter 3

Explanatory and response variables

1. Define explanatory and response variables in terms of their roles in predicting one variable from another.
2. Given a predictive question about two variables, identify which variable is the explanatory variable and which is the response variable.

Relationship between two quantitative variables: scatterplots

1. Interpret a scatter plot showing the relationship between an explanatory variable and response variable in a data set.
2. Construct a scatter plot from a (small) data set with two quantitative variables.

Adding categorical variables to scatterplots

1. Explain how to construct a scatter plot that also includes the value of a categorical variable for each individual in a data set.

Measuring linear association: correlation

1. State the formula for the sample covariance and sample correlation.
2. State the major properties of the sample correlation.
3. Given a scatter plot, identify whether the sample correlation for the points is positive, negative, or nearly zero.

Nonlinear Relationships (Handout)

1. Explain why it is sometimes appropriate to transform either the explanatory variable, the response variable, or both, before computing a sample correlation.

R

1. Construct a scatter plot using mosaic in R.
2. Construct a scatter plot that includes a categorical variable using mosaic in R.
3. Compute the sample correlation between two variables using mosaic in R.
4. Compute \(\log_{10} (x)\) in R.
5. Compute the sample correlation between transformed explanatory and response variables.