Essentials of Modern Business Statistics with Microsoft Office Excel (Book Only)
Essentials of Modern Business Statistics with Microsoft Office Excel (Book Only)
7th Edition
ISBN: 9781337298353
Author: David R. Anderson, Dennis J. Sweeney, Thomas A. Williams
Publisher: South-Western College Pub
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Chapter 15, Problem 15.2.2CP

Predicting Winnings for NASCAR Drivers

Matt Kenseth won the 2012 Daytona 500, the most important race of the NASCAR season. His win was no surprise because for the 2011 season he finished fourth in the point standings with 2330 points, behind Tony Stewart (2403 points), Carl Edwards (2403 points), and Kevin Harvick (2345 points). In 2011 he earned $6,183,580 by winning three Poles (fastest driver in qualifying), winning three races, finishing in the top five 12 times, and finishingin the top ten 20 times. NASCAR's point system in 2011 allocated 43 points to the driver who finished first, 42 points to the driver who finished second, and so on down to 1 point for the driver who finished in the 43rd position. In addition, any driver who led a lap received 1 bonus point, the driver who led the most laps received an additional bonus point, and the race winner was awarded 3 bonus points. But the maximum number of points a driver could earn in any race was 48. Table 15.8 shows data for the 2011 season for the top 35 drivers (NASCAR website, February 28, 2011).

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2. Develop an estimated regression equation that can be used to predict Winnings ($) given the number of poles won (Poles), the number of wins (Wins), the number of top five finishes (Top 5), and the number of top ten (Top 10) finishes. Test for individual significance and discuss your findings and conclusions.

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For each of the time​ series, construct a line chart of the data and identify the characteristics of the time series​ (that is,​ random, stationary,​ trend, seasonal, or​ cyclical). Year    Month    Rate (%)2009    Mar    8.72009    Apr    9.02009    May    9.42009    Jun    9.52009    Jul    9.52009    Aug    9.62009    Sep    9.82009    Oct    10.02009    Nov    9.92009    Dec    9.92010    Jan    9.82010    Feb    9.82010    Mar    9.92010    Apr    9.92010    May    9.62010    Jun    9.42010    Jul    9.52010    Aug    9.52010    Sep    9.52010    Oct    9.52010    Nov    9.82010    Dec    9.32011    Jan    9.12011    Feb    9.02011    Mar    8.92011    Apr    9.02011    May    9.02011    Jun    9.12011    Jul    9.02011    Aug    9.02011    Sep    9.02011    Oct    8.92011    Nov    8.62011    Dec    8.52012    Jan    8.32012    Feb    8.32012    Mar    8.22012    Apr    8.12012    May    8.22012    Jun    8.22012    Jul    8.22012    Aug    8.12012    Sep    7.82012    Oct…
For each of the time​ series, construct a line chart of the data and identify the characteristics of the time series​ (that is,​ random, stationary,​ trend, seasonal, or​ cyclical). Date    IBM9/7/2010    $125.959/8/2010    $126.089/9/2010    $126.369/10/2010    $127.999/13/2010    $129.619/14/2010    $128.859/15/2010    $129.439/16/2010    $129.679/17/2010    $130.199/20/2010    $131.79 a. Construct a line chart of the closing stock prices data. Choose the correct chart below.
For each of the time​ series, construct a line chart of the data and identify the characteristics of the time series​ (that is,​ random, stationary,​ trend, seasonal, or​ cyclical) Date    IBM9/7/2010    $125.959/8/2010    $126.089/9/2010    $126.369/10/2010    $127.999/13/2010    $129.619/14/2010    $128.859/15/2010    $129.439/16/2010    $129.679/17/2010    $130.199/20/2010    $131.79
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