Essentials Of Business Analytics
1st Edition
ISBN: 9781285187273
Author: Camm, Jeff.
Publisher: Cengage Learning,
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Textbook Question
Chapter 4, Problem 17P
A sample containing years to maturity and (percent) yield for 40 corporate bonds is contained in the file named CorporateBonds (Barron’s, April 2. 2012).
- a. Develop a scatter chart of the data using years to maturity as the independent variable. Does a simple linear regression model appear to be appropriate?
- b. Develop an estimated
quadratic regression equation with years to maturity and squared values of years to maturity as the independent variables. How much variation in the sample values of yield 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? - c. Create a plot of the linear and quadratic regression lines overlaid on the scatter chart of years to maturity and yield. Does this helps you better understand the difference in how the quadratic regression model and a simple linear regression model fit the sample data? Which model does this chart suggest provides a superior fit to the sample data?
- d. What other independent variables could you include in your regression model to explain more variation in yield?
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A researcher notes that, in a certain region, a disproportionate number of software millionaires were born around the year 1955. Is this a coincidence, or does birth year matter when gauging whether a software founder will besuccessful? The researcher investigated this question by analyzing the data shown in the accompanying table. Complete parts a through c below.
a. Find the coefficient of determination for the simple linear regression model relating number (y) of software millionaire birthdays in a decade to total number (x) of births in the region. Interpret the result.
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If a scatterplot is created in excel, and a line of regression is fit along with a derived functional form, what does it mean to describe and interpret them? What conclusions would be made about relationships between two recorded variables?
Suppose that a kitchen cabinet warehouse company would like to be able to predict the area of a customer’s kitchen using the number of cabinets and the kitchen ceiling height. To do so data is collected on the following variables from a random sample of customers:
Area – area of the kitchen in square feet
Height – ceiling height in the kitchen (from floor to ceiling) in inches
Cabinets – number of cabinets in the kitchen
Suppose that a multiple linear regression model was fit to the data and that the following output resulted:
Coefficients:
(Intercept)HeightCabinets
Estimate-57.98771.2760.3393
Std. Error8.63820.26430.1302
t value -6.7134.8282.607
Pr(>|t|)2.75e-074.44e-050.0145
What is the predicted area of a kitchen with a height of 96 inches and 10 cabinets? Report your answer to 1 decimal place.
square feet
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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- Olympic 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_forwardSuppose that a kitchen cabinet warehouse company would like to be able to predict the area of a customer’s kitchen using the number of cabinets and the kitchen ceiling height. To do so data is collected on the following variables from a random sample of customers: Area – area of the kitchen in square feet Height – ceiling height in the kitchen (from floor to ceiling) in inches Cabinets – number of cabinets in the kitchen Suppose that a multiple linear regression model was fit to the data and that the following output resulted: Coefficients: (Intercept)HeightCabinets Estimate-57.98771.2760.3393 Std. Error8.63820.26430.1302 t value -6.7134.8282.607 Pr(>|t|)2.75e-074.44e-050.0145 10 Question 10 This is not a form; we suggest that you use the browse mode and read all parts of the question carefully. Which of the following is the correct interpretation of the coefficient for Cabinets? For a kitchen with a given ceiling height, the average number of cabinets…arrow_forwardSuppose that a kitchen cabinet warehouse company would like to be able to predict the area of a customer’s kitchen using the number of cabinets and the kitchen ceiling height. To do so data is collected on the following variables from a random sample of customers: Area – area of the kitchen in square feet Height – ceiling height in the kitchen (from floor to ceiling) in inches Cabinets – number of cabinets in the kitchen Suppose that a multiple linear regression model was fit to the data and that the following output resulted: Coefficients: (Intercept)HeightCabinets Estimate-57.98771.2760.3393 Std. Error8.63820.26430.1302 t value -6.7134.8282.607 Pr(>|t|)2.75e-074.44e-050.0145 Why is the interpretation of the constant term (i.e. "intercept") not meaningful for this example? The predicted area will be negative when the number of cabinets is zero and the height of the kitchen is also zero. But we cannot have a negative area, nor a kitchen ceiling height of 0 inches.…arrow_forward
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