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Chapter 3: Q.27 (page 162)

You have data for many years on the average price of a barrel of oil and the average retail price of a gallon of unleaded regular gasoline. If you want to see how well the price of oil predicts the price of gas, then you should make a scatterplot with ______ as the explanatory variable.

(a) the price of oil

(b) the price of gas

(c) the year

(d) either oil price or gas price

(e) time

Short Answer

Expert verified

The correct option is A (the price of oil).

Step by step solution

01

Given Information

You have data for many years on the average price of a barrel of oil and the average retail price of a gallon of unleaded regular gasoline. If you want to see how well the price of oil predicts the price of gas, then you should make a scatterplot with ______ as the explanatory variable.

02

Explanation

Make the variable you want to be the "predictor" the explanatory variable if you want to examine how well one variable predicts another in a scatterplot.

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Most popular questions from this chapter

Late bloomers? Japanese cherry trees tend to blossom early when spring weather is warm and later when spring weather is cool. Here are some data on the average March temperature (in °C) and the day in April when the first cherry blossom appeared over a 24-year period:

(a) Make a well-labeled scatterplot that’s suitable for predicting when the cherry trees will bloom from the temperature. Describe the direction, form, and strength of the relationship.

(b) Use technology to find the equation of the least-squares regression line. Interpret the slope and y the intercept of the line in this setting.

(c) The average March temperature this year was 3.5°C When would you predict that the first cherry blossom would appear? Show your method clearly.

(d) Find the residual for the year when the average March temperature was 4.5°C Show your work.

(e) Use technology to construct a residual plot. Describe what you see.

(f) Find and interpret the value of r2 and s in this setting.

Least-squares idea The table below gives a small set of data. Which of the following two lines fits the data better: y^=1−xory^=3−2x? Make a graph of the data and use it to help justify your answer. (Note: Neither of these two lines is the least-squares regression line for these data.)

We expect a car's highway gas mileage to be related to its city gas mileage. Data for all 1198vehicles in the government's 2008Fuel Economy Guide give the regression line predicted highway mpg=4.62+1.109(city mpg).

(a) What’s the slope of this line? Interpret this value in context.

(b) What’s the intercept? Explain why the value of the intercept is not statistically meaningful.

(c) Find the predicted highway mileage for a car that gets 16miles per gallon in the city. Do the same for a car with a city mileage of 28mpg.

How much gas? In Exercise 4(page 158), we examined the relationship between the average monthly temperature and the amount of natural gas consumed in Joan’s midwestern home. The figure below shows the original scatterplot with the least-squares line added. The equation of the least-squares line isy^=1425−19.87x

(a) Identify the slope of the line and explain what it means in this setting.

(b) Identify the y-intercept of the line. Explain why it’s risky to use this value as a prediction.

(c) Use the regression line to predict the amount of natural gas Joan will use in a month with an average temperature of 30°F.

When it rains, it pours The figure below plots the record-high yearly precipitation in each state against that state’s record-high 24-hour precipitation. Hawaii is a high outlier, with a record-high yearly record of 704.83 inches of rain recorded at Kukui in 1982

(a) The correlation for all 50 states in the figure is 0.408 If we leave out Hawaii, would the correlation increase, decrease, or stay the same? Explain.

(b) Two least-squares lines are shown on the graph. One was calculated using all 50 states, and the other omits Hawaii. Which line is which? Explain.

(c) Explain how each of the following would affect the correlation, s, and the least-squares line:

  • Measuring record precipitation in feet instead of inches for both variables.
  • Switching the explanatory and response variables.
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