Unit 2: Exploring Two-Variable Data

A researcher collects data on the number of hours students spend on social media per day ([math]) and their GPA ([math]) for 8 students:

A study examines the relationship between the age of a used car (in years, [math]) and its selling price (in thousands of dollars, [math]). The LSRL is [math]. Additional info: [math], [math], [math], [math], [math].

The LSRL for predicting exam score ([math]) from study hours ([math]) is [math]. Data for 6 students:

The population of bacteria ([math], in thousands) measured every hour ([math]):

A regression analysis on SAT math scores ([math]) and college GPA ([math]) for 12 students yields [math], [math]. Suppose a 13th student with an SAT math of 800 and college GPA of 1.5 is added to the data set.

A statistics software produces the following regression output for predicting weight ([math], in pounds) from height ([math], in inches): Predictor Coef SE Coef T P Constant -210.5 25.8 -8.16 0.000 Height 5.10

A study examines the relationship between shoe size ([math]) and reading ability ([math], measured by a standardized test). The data shows a strong positive correlation ([math]). The participants include both children (ages 5–12) and adults (ages 25–

A researcher fits a linear regression model to predict crop yield ([math], bushels per acre) from rainfall ([math], inches) using data from 20 fields.