Project Linear Regression: Matt Kenseth won the 2012 Daytona 500, the most important race of the NASCAR season. Matts win was no surprise because for the 2011 season Matt finished fourth in the point standings with 2,330 points, behind Tony Stewart (2,403 points), Carl Edwards (2,403 points), and Kevin Harvick (2,345 points). In 2011 Matt earned $6,183,580 by winning three Poles (fastest driver in qualifying), winning three races, finishing in the top five 12 times, and finishing in the top ten 20 times. NASCARs point system in 2011 allocated 43 points to the driver who finished first, 42 points to the driver who finished second, and so on down to 1 point for the driver who finished in the 43rd position. In addition, any driver who led a lap received 1 bonus point, the driver who led the most laps received an additional bonus point, and the race winner was awarded 3 bonus points. But the maximum number of points a driver could earn in any race was 48. The following table shows data for the 2011 season for the top 35 drivers (NASCAR website). The data is contained in the file Linear Regression Case Study Data.xls Overview: Construct an estimated simple linear regression model that estimates how a dependent variable is related to an independent variable., Construct an estimated multiple linear regression model that estimates how a dependent variable is related to multiple independent variables., Compute and interpret the estimated coefficient of determination for a linear regression model., Assess whether the conditions necessary for valid inference in a least squares linear regression model are satisfied and test hypotheses about the parameters., Test hypotheses about the parameters of a linear regression model and interpret the results of these hypotheses tests. Instructions: Build both an excel file (use the data file provided) and make separate tabs for each section to complete the following report. The management report will consist of the following sections: 1) Suppose you wanted to predict Winnings ($) using only the number of poles won (Poles), the number of wins (Wins), the number of top five finishes (Top 5), or the number of top ten finishes (Top 10). Which of these four variables provides the best single predictor of winnings? 2) Develop an estimated linear regression equation that can be used to predict Winnings ($) given the number of poles won (Poles), the number of wins (Wins), the number of top five finishes (Top 5), and the number of top ten finishes (Top 10). Test for individual significance and discuss your findings and conclusions. 3) Create two new independent variables: Top 25 and Top 610. Top 25 represents nascar the number of times the driver finished between second and fifth place and Top 610 represents the number of times the driver finished between sixth and tenth place. Develop an estimated linear regression equation that can be used to predict Winnings ($) using Poles, Wins, Top 25, and Top 610. Test for individual significance and discuss your findings and conclusions. 4) Based upon the results of your analysis, which estimated linear regression equation would you recommend using to predict Winnings ($)? Provide an interpretation of the estimated coefficients for this equation. Once these four sections are completed in Excel (try to use tabs to organize data including tab labels), then write a report outlining the following: 1) The background indicating why the information you are proposing is useful. 2) Outline and explain: a. Provide executive summary of each (1 through 4) type of analysis along with the results. b. Provide interpretation of these results (outlined in 1 through 4 above). 3) Conclusion section: Provide a conclusion section as to which model was used and how you recommend or propose a better model for predicting winnings by driver.

Driver	Points	Poles	Wins	Top 5	Top 10	Winnings ($)
Tony Stewart	2403	1	5	9	19	6,529,870
Carl Edwards	2403	3	1	19	26	8,485,990
Kevin Harvick	2345	0	4	9	19	6,197,140
Matt Kenseth	2330	3	3	12	20	6,183,580
Brad Keselowski	2319	1	3	10	14	5,087,740
Jimmie Johnson	2304	0	2	14	21	6,296,360
Dale Earnhardt Jr.	2290	1	0	4	12	4,163,690
Jeff Gordon	2287	1	3	13	18	5,912,830
Denny Hamlin	2284	0	1	5	14	5,401,190
Ryan Newman	2284	3	1	9	17	5,303,020
Kurt Busch	2262	3	2	8	16	5,936,470
Kyle Busch	2246	1	4	14	18	6,161,020
Clint Bowyer	1047	0	1	4	16	5,633,950
Kasey Kahne	1041	2	1	8	15	4,775,160
A.J. Allmendinger	1013	0	0	1	10	4,825,560
Greg Biffle	997	3	0	3	10	4,318,050
Paul Menard	947	0	1	4	8	3,853,690
Martin Truex Jr.	937	1	0	3	12	3,955,560
Marcos Ambrose	936	0	1	5	12	4,750,390
Jeff Burton	935	0	0	2	5	3,807,780
Juan Montoya	932	2	0	2	8	5,020,780
Mark Martin	930	2	0	2	10	3,830,910
David Ragan	906	2	1	4	8	4,203,660
Joey Logano	902	2	0	4	6	3,856,010
Brian Vickers	846	0	0	3	7	4,301,880
Regan Smith	820	0	1	2	5	4,579,860
Jamie McMurray	795	1	0	2	4	4,794,770
David Reutimann	757	1	0	1	3	4,374,770
Bobby Labonte	670	0	0	1	2	4,505,650
David Gilliland	572	0	0	1	2	3,878,390
Casey Mears	541	0	0	0	0	2,838,320
Dave Blaney	508	0	0	1	1	3,229,210
Andy Lally*	398	0	0	0	0	2,868,220
Robby Gordon	268	0	0	0	0	2,271,890
J.J. Yeley	192	0	0	0	0	2,559,500