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Essentials Of Business Analytics
1st Edition
ISBN: 9781285187273
Author: Camm, Jeff.
Publisher: Cengage Learning,
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Textbook Question
Chapter 4, Problem 16P
A highway department is studying the relationship between traffic flow and speed during rush hour on Highway 193. The data in the file TrafficFlow were collected on Highway 193 during 100 recent rush hours.
- a. Develop a scatter chart for these data. What does the scatter chart indicate about the relationship between vehicle speed and traffic flow?
- b. Develop an estimated simple linear regression equation for the data. How much variation in the sample values of traffic flow is explained by this regression model? Use a 0.05 level of significance to test the relationship between vehicle speed and traffic flow. What is the interpretation of this relationship?
- c. Develop an estimated
quadratic regression equation for the data. How much variation in the sample values of traffic flow is explained by this regression model? Test the relationship between each of the independent variables and the dependent variable at a 0.05 level of significance. How would you interpret this model? Is this model superior to the model you developed in part (b)? - d. As an alternative to fitting a second-order model, fit a model using a piecewise linear regression with a single knot. What value of vehicle speed appears to be a good point for the placement of the knot? Does the estimated piecewise linear regression provide a better fit than the estimated quadratic regression developed in part (c)? Explain.
- e. Separate the data into two sets such that one data set contains the observations of vehicle speed less than the value of the knot from part (d) and the other data set contains the observations of vehicle speed greater than or equal to the value of the knot from part (d). Then fit a simple linear regression equation to each data set. How does this pair of regression equations compare to the single piecewise linear regression with the single knot from part (d)? In particular, compare predicted values of traffic flow for values of the speed slightly above and slightly below the knot value from part (d).
- f. What other independent variables could you include in your regression model to explain more variation in traffic flow?
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Chapter 4 Solutions
Essentials Of Business Analytics
Ch. 4 - Bicycling World, a magazine devoted to cycling,...Ch. 4 - In a manufacturing process the assembly line speed...Ch. 4 - Jensen Tire Auto is deciding whether to purchase...Ch. 4 - A sociologist was hired by a large city hospital...Ch. 4 - The regional transit authority for a major...Ch. 4 - A marketing professor at Givens College is...Ch. 4 - The Dow Jones Industrial Average (DJIA) and the...Ch. 4 - The Toyota Camry is one of the best-selling cars...Ch. 4 - Dixie Showtime Movie Theaters, Inc., owns and...Ch. 4 - Prob. 10P
Ch. 4 - The American Association of Individual Investors...Ch. 4 - The National Football League (NFL) records a...Ch. 4 - Johnson Filtration. Inc., provides maintenance...Ch. 4 - A study investigated the relationship between...Ch. 4 - The U.S. Department of Energys Fuel Economy Guide...Ch. 4 - A highway department is studying the relationship...Ch. 4 - A sample containing years to maturity and...Ch. 4 - In 2011, home prices and mortgage rates fell so...Ch. 4 - A recent 10-year study conducted by a research...Ch. 4 - The Scholastic Aptitude Test (or SAT) is a...Ch. 4 - Consider again the example introduced in Section...Ch. 4 - Alumni donations are an important source of...
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- Does Table 1 represent a linear function? If so, finda linear equation that models the data.arrow_forwardOlympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?arrow_forward
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