Chapter 4: Q. 16 (page 192)
A positive linear relationship between two variables means that one variable tends to increase linearly as the other------
Short Answer
Increases
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Chapter 4: Q. 16 (page 192)
A positive linear relationship between two variables means that one variable tends to increase linearly as the other------
Increases
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4.85 A measure of the amount of variation in the observed values of the response variable explained by the regression is the-----. The mathematical abbreviation for it is----.
In Exercise 4.7, we give linear equations. For each equation,
a. find the -intercept and slope.
b. determine whether the line slopes upward, slopes downward, or is horizontal, without graphing the equation.
c. use two points to graph the equation.
given equation is,
A value of close to indicates that the regression equation is either useless or ----- for making predictions.
Corvette Prices. In Exercise 4.59. you determined a regression equation that can be used to predict the price of a Corvette, given its age.
a. Should that regression equation be used to predict the price of a 4-year-old Corvette? a 10-year-old Corvette? Explain your answers.
b. For which ages is the use of the regression equation to predict price reasonable?
Tine Series. A collection of observations of a variable y taken at regular intervals over time is called a time series. Bocoomsic data and electrical signals are examples of time series. We can think of a time series as providing data points where is the ith observation time and is the observed value of y at time . If a time series exhibits a linear trend, we can find that trend by determining the regression equation for the data points. We can then use the regression equation for forecasting purposes.
As an illustration, consider the data on the WeissStats site that shows the U.S. population, in millions of persons, for the years 1900 2013. as provided by the I.S. Census Beret.
a. Use the technology of your choice to lesbian a scatterplot of the data.
h. Use the technology of your choice to find the regression equation.
6. Use your result from part (b) to forecast the U.S. population for the years 2014 and 2015 .
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