![MATLAB: An Introduction with Applications](https://www.bartleby.com/isbn_cover_images/9781119256830/9781119256830_smallCoverImage.gif)
MATLAB: An Introduction with Applications
6th Edition
ISBN: 9781119256830
Author: Amos Gilat
Publisher: John Wiley & Sons Inc
expand_more
expand_more
format_list_bulleted
Question
Based upon the results of your analysis, what estimated regression equation would you recommend using to predict Winnings ($)? Yes, ◦P-values from the regression table: ◦Poles = 0.9047 > 0.01 (not significant) ◦Wins = 0.9039 > 0.01 (not significant) ◦Top 5 = 0.1677 > 0.01 (not significant) ◦Top 10 = 0.0015 < 0.01 (significant) ◦Conclusion: Top 10 provides the best single predictor of winnings Provide an interpretation of the estimated regression coefficients for this equation. |
Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | |
Intercept | 3140367.09 | 184229.02 | 17.05 | 0.0000 | 2764121.22 | 3516612.95 | 2764121.22 | 3516612.95 |
Poles | -12938.92 | 107205.08 | -0.12 | 0.9047 | -231880.89 | 206003.05 | -231880.89 | 206003.05 |
Wins | 13544.81 | 111226.22 | 0.12 | 0.9039 | -213609.43 | 240699.05 | -213609.43 | 240699.05 |
Top 5 | 71629.39 | 50666.87 | 1.41 | 0.1677 | -31846.16 | 175104.94 | -31846.16 | 175104.94 |
Top 10 | 117070.58 | 33432.88 | 3.50 | 0.0015 | 48791.52 | 185349.63 | 48791.52 |
185349.63
|
Driver | Points | Poles | Wins | Top 5 | Top 10 | Winnings ($) |
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 |
Expert Solution
![Check Mark](/static/check-mark.png)
This question has been solved!
Explore an expertly crafted, step-by-step solution for a thorough understanding of key concepts.
This is a popular solution
Trending nowThis is a popular solution!
Step by stepSolved in 3 steps with 3 images
![Blurred answer](/static/blurred-answer.jpg)
Knowledge Booster
Similar questions
- A car lot wants to predict the # of car accessories they need to sell monthly based on the number of cars sold. The car lot owner randomly selects 12 months of data. The simple linear regression equation is ý = 14 - 2x. The coefficient of determination is 0.6364. What is the correlation coefficient to describe the relationship between these two variables? Multiple Choicearrow_forwardA regression was run to determine if there is a relationship between hours of study per week (x) and the final exam scores (y). The results of the regression were: y=ax+b a=5.865 b=25.06 r²=0.81 r=0.9 Use this to predict the final exam score of a student who studies 4.5 hours per week, and please round your answer to a whole number.arrow_forwardA regression was run to determine if there is a relationship between hours of study per week (x) and the final exam Scores (y). The results of the regression were: y=ax+b a=6.179 b=28.96 r²=0.937024 r=0.968 Use this to predict the final exam score of a student who studies 4 hours per week, and please round your answer to a whole number.arrow_forward
- A regression was run to determine if there is a relationship between hours of study per week (X) and the test scores (y ). The results of the regression were:y=ax+ba=5.931b=20.1r2=0.748225r=0.865Use this to predict the final exam score of a student who studies 3 hours per week, and please round your answer to a whole number.arrow_forwardUse the estimate yest=3.3x2 - 9.4x - 0.1 to calculate a set of residuals, using a formula or a calculator. Explain your steps, then create a residual plot using this new model and assess its validityarrow_forwardThe multiple regression describes how the mean value of y is related to the xi independent variables. The parameters ?i are used to describe how the mean value of y changes for a one-unit increase in xi when the other variables are held constant. The given estimated regression equation follows where x1 is the high-school grade point average, x2 is the SAT mathematics score, and y is the final college grade point average. ŷ = −1.38 + 0.0232x1 + 0.00482x2 If the variable x2 is held constant, then only changes in x1 will impact the predicted values of ŷ. Since the coefficient of x1 is positive, for each one-unit increase of x1, the values of ŷ will increase by the value of ?1, where ?1 = . In context, for each one point increase of the high-school grade point average, the final college grade point average will increase by this amount when the SAT mathematics score does not change. If the variable x1 is held constant, then only changes in x2 will impact the predicted values of ŷ. Since the…arrow_forward
- In a regression analysis if SSE = 64 and SSR = 364, then what is the coefficient of determination? (please keep 2 decimal places)arrow_forwardSuppose the following estimated regression equation was determined to predict salary based on years of experience. Estimated Salary = 29,136.63 +2257.51(Years of Experience) What is the estimated salary for an employee with 24 years of experience? Answer Keypad Keyboard Shortcuts Tablesarrow_forwardplease answer the third and fourth partarrow_forward
- Using the data in the Excel spreadsheet "auto.xlsx", estimate a regression to answer the following question. How much does a 1 inch increase in LENGTH corresponds to a _________________ dollars increase in PRICE. Use negative values if it results in a decreased price. Round your answers to two decimals.arrow_forwardA regression was run to determine if there is a relationship between hours of study per week (xx) and the test scores (yy).The results of the regression were:y=ax+b a=6.686 b=35.26 r2=0.6084 r=0.78 Use this to predict the final exam score of a student who studies 8.5 hours per week, and please round your answer to a whole number.arrow_forward
arrow_back_ios
arrow_forward_ios
Recommended textbooks for you
- MATLAB: An Introduction with ApplicationsStatisticsISBN:9781119256830Author:Amos GilatPublisher:John Wiley & Sons IncProbability and Statistics for Engineering and th...StatisticsISBN:9781305251809Author:Jay L. DevorePublisher:Cengage LearningStatistics for The Behavioral Sciences (MindTap C...StatisticsISBN:9781305504912Author:Frederick J Gravetter, Larry B. WallnauPublisher:Cengage Learning
- Elementary Statistics: Picturing the World (7th E...StatisticsISBN:9780134683416Author:Ron Larson, Betsy FarberPublisher:PEARSONThe Basic Practice of StatisticsStatisticsISBN:9781319042578Author:David S. Moore, William I. Notz, Michael A. FlignerPublisher:W. H. FreemanIntroduction to the Practice of StatisticsStatisticsISBN:9781319013387Author:David S. Moore, George P. McCabe, Bruce A. CraigPublisher:W. H. Freeman
![Text book image](https://www.bartleby.com/isbn_cover_images/9781119256830/9781119256830_smallCoverImage.gif)
MATLAB: An Introduction with Applications
Statistics
ISBN:9781119256830
Author:Amos Gilat
Publisher:John Wiley & Sons Inc
![Text book image](https://www.bartleby.com/isbn_cover_images/9781305251809/9781305251809_smallCoverImage.gif)
Probability and Statistics for Engineering and th...
Statistics
ISBN:9781305251809
Author:Jay L. Devore
Publisher:Cengage Learning
![Text book image](https://www.bartleby.com/isbn_cover_images/9781305504912/9781305504912_smallCoverImage.gif)
Statistics for The Behavioral Sciences (MindTap C...
Statistics
ISBN:9781305504912
Author:Frederick J Gravetter, Larry B. Wallnau
Publisher:Cengage Learning
![Text book image](https://www.bartleby.com/isbn_cover_images/9780134683416/9780134683416_smallCoverImage.gif)
Elementary Statistics: Picturing the World (7th E...
Statistics
ISBN:9780134683416
Author:Ron Larson, Betsy Farber
Publisher:PEARSON
![Text book image](https://www.bartleby.com/isbn_cover_images/9781319042578/9781319042578_smallCoverImage.gif)
The Basic Practice of Statistics
Statistics
ISBN:9781319042578
Author:David S. Moore, William I. Notz, Michael A. Fligner
Publisher:W. H. Freeman
![Text book image](https://www.bartleby.com/isbn_cover_images/9781319013387/9781319013387_smallCoverImage.gif)
Introduction to the Practice of Statistics
Statistics
ISBN:9781319013387
Author:David S. Moore, George P. McCabe, Bruce A. Craig
Publisher:W. H. Freeman