A sample of 15 golfers who played a golf course on a certain day was selected. For each golfer, the average drivingdistance (a), in yards, and the percent of fairways hit on the drive (y) were recorded.The scatterplot displays the percent of fairways hit versus the average driving distance. Also shown is the least-squares regression line, ŷ =66.228 +0.0002x.85%80%-75%70%Percent of65%Fairways Hit60%55%50%45%220 225 230 235 240 245 250 255 260Average Driving Distance (yards)The point circled on the scatterplot is considered an influential point. A new least-squares regression line will becalculated with the influential point removed. How will the removal of the influential point affect the new least-squares regression line for the remaining 14 points?(A) The y-intercept will remain the same, and the slope will be negative.(B) The y-intercept will decrease, and the slope will be negative.(C) The y-intercept will decrease, and the slope will be positive.(D) The y-intercept will increase, and the slope will be negative.(E) The y-intercept will increase, and the slope will be positive.

Considering the figure presented above, the outlier lies below the regression line which tells us…

Answered: A sample of 15 golfers who played a…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

The datasetBody.xlsgives the percent of weight made up of body fat for 100 men as well as other variables such as Age, Weight (lb), Height (in), and circumference (cm) measurements for the Neck, Chest, Abdomen, Ankle, Biceps, and Wrist. We are interested in predicting body fat based on abdomen circumference. Find the equation of the regression line relating to body fat and abdomen circumference. Make a scatter-plot with a regression line. What body fat percent does the line predict for a person with an abdomen circumference of 110 cm? One of the men in the study had an abdomen circumference of 92.4 cm and a body fat of 22.5 percent. Find the residual that corresponds to this observation. Bodyfat Abdomen 32.3 115.6 22.5 92.4 22 86 12.3 85.2 20.5 95.6 22.6 100 28.7 103.1 21.3 89.6 29.9 110.3 21.3 100.5 29.9 100.5 20.4 98.9 16.9 90.3 14.7 83.3 10.8 73.7 26.7 94.9 11.3 86.7 18.1 87.5 8.8 82.8 11.8 83.3 11 83.6 14.9 87 31.9 108.5 17.3…
A pediatrician wants to determine the relation that exists between a child's height, x, and head circumference, v. She randomly selects 11 children from her practice, measures their heights and head circumferences, and obtains the accompanying data. Complete parts (a) through (g) below. A Click the icon to view the children's data. (a) Find the least-squares regression line treating height as the explanatory variable and head circumference as the response variable. y=x+ (D (Round the slope to three decimal places and round the constant to one decimal place as needed.) (b) Interpret the slope and y-intercept, if appropriate. First interpret the slope. Select the correct choice below and, if necessary. in the answer box to complete your choice. O A. For every inch increase in height, the head circumference increases by (Round to three decimal places as needed.) in., on average. O B. For a height of 0 inches, the head circumference is predicted to be (Round to three decimal places as…
A candy bar manufacturer is interested in trying to estimate how sales are influenced by the price of their product. To do this, the company randomly chooses 6 small cities and offers the candy bar at different prices. Using candy bar sales as the dependent variable, the company will conduct a simple linear regression on the data below: River Falls Hudson Ellsworth Prescott Rock Elm Stillwater Select one: a. 78.39% b. 88.54% O c. 161.39% O d. 100% City What percentage of the total variation in the candy bar sales is explained by prices? e. 48.19% Price ($) 1.30 1.60 1.80 2.00 2.40 2.90 Sales 100 90 90 40 38 32
Is It Getting Harder to Win a Hot Dog Eating Contest?Every Fourth of July, Nathan’s Famous in New York City holds a hot dog eating contest. The table below shows the winning number of hot dogs and buns eaten every year from 2002 to 2015, and the data are also available in HotDogs. The figure below shows the scatterplot with the regression line. Year Hot Dogs 2015 62 2014 61 2013 69 2012 68 2011 62 2010 54 2009 68 2008 59 2007 66 2006 54 2005 49 2004 54 2003 45 2002 50 Winning number of hot dogs in the hot dog eating contest Winning number of hot dogs and buns Click here for the dataset associated with this question. (a) Is the trend in the data mostly positive or negative? Positive Negative (b) Using the figure provided, is the residual larger in 2007 or 2008?Choose the answer from the menu in accordance to item (b) of the question statement 20072008 Is the residual positive or…
a) Write out the regression equation. b) Fill in the missing values *, **, *** and ****. c) Use the p-value approach to determine if ? is significant at the 5% significance level
In baseball, two statistics, the ERA (Earned Run Average) and the WHIP (Walks and Hits per Inning Pitched), are used to measure the quality of pitchers. For both measures, smaller values indicate higher quality. The following computer output gives the results from predicting ERA by using WHIP in a least-squares regression for the 2017 baseball season. Variable DF Estimate SE T Intercept 1 -5.0 0.26 - 19.3 WHIP 1 6.8 0.14 47.4 Which of the following statements is the best interpretation of the value 6.8 shown in the output? ERA is predicted to increase by 6.8 units for each 1 unit increase of WHIP. WHIP is predicted to increase by 6.8 units for each 1 unit increase of ERA. For a pitcher with 0 units of WHIP, the ERA is predicted to be approximately 6.8 units. For a pitcher with 0 units of ERA, the WHIP is predicted to be approximately 6.8 units. Approximately 6.8% of the variability in ERA is due to its linear relationship with WHIP.
Report the equation of the regression line and interpret it in the context of the problem
Use the following information to answer questions 6 and 7. In a study, nine tires of a particular brand were driven on a track under identical conditions. Each tire was driven a particular controlled distance (measured in thousands of miles) and the tread depth was measured after the drive. Tread depth is measured in "mils." Here, 1 mil is 0.001 inch. The least-squares regression line was computed and added to a scatterplot of these data. On the plot, one data point is marked with an "X." The equation of the least-squares regression line is: Tread depth = 360.64 - 11.39 Miles The data value marked with "X" in the provided scatterplot has Tread Depth (Mils) 60 80 150 0 5 O a negative value for the residual. O a positive value for the residual. O a zero value for the residual. O a zero value for the correlation. 10 15 Miles (x 1000) 20 25 30
The table below shows the heights (inches) and weights (pounds) of 9 randomly selected children. Child Data Height (inches), x 36 56 58 59 61 63 65 69 73 Weight (pounds), y 69 89 96 74 110 90 123 110 128 (a) Construct a scatter diagram for the data, where the weight of the child is the response variable. (b) Determine the least-squares regression line for the data and graph the line on the scatter diagram from part (a). (use calculator) (show what did you do on the calculator) (c) Interpret the slope and the y–intercept in the context of the problem. (d) Predict the weight of a child that is 57 inches tall. Comparing with the observed value, what is the residual? Is the observed weight above or below average? (e) Determine the correlation coefficient, r. Is there a correlation? Discuss the strength of the correlation and why. (f) Are heights and weights of children linearly related? Why or why not? Be sure to justify your answer by using the appropriate method. (g)…
c. Which of the following is the correct graph of the given data? -ОА. 10 1012 12 10 O C. 12- 81012 1012 Using the graph, explain the dramatic difference between the answers to parts (a) and (b). Choose the correct answer below. OA. The point (10,10) is not an outier, but it does have a strong effect on the least squares line and the correlation coefficient. OB. The point (10,10) does not have any effect on the least squares line and the correlation coefficient. OC. The point (10,10) is an outler that has a weak effect on the least squares line and the correlation coefficient. OD. The point (10,10) is an outlier that has a strong effect on the least squares line and the correlation coefficient.
Biologist Theodore Garland, Jr. studied the relationship between running speeds and morphology of 49 species of cursorial mammals (mammals adapted to or specialized for running). One of the relationships he investigated was maximal sprint speed in kilometers per hour and the ratio of metatarsal-to-femur length. A least-squares regression on the data he collected produces the equation ŷ = 37.67 + 33.18x %3D where x is metatarsal-to-femur ratio and ŷ is predicted maximal sprint speed in kilometers per hour. The standard error of the intercept is 5.69 and the standard error of the slope is 7.94. Construct an 80% confidence interval for the slope of the population regression line. Give your answers precise to at least two decimal places. Lower limit: Upper limit:
Table 7 gives the number of visitors per year at YosemiteNational Park.(a) Find the least-squares line for these data.(b) Estimate the number of visitors in 2017.

Recommended textbooks for you

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

Elementary Statistics: Picturing the World (7th E…

Statistics

ISBN:

9780134683416

Author:

Ron Larson, Betsy Farber

Publisher:

PEARSON

The Basic Practice of Statistics

Statistics

ISBN:

9781319042578

Author:

David S. Moore, William I. Notz, Michael A. Fligner

Publisher:

W. H. Freeman

Introduction to the Practice of Statistics

Statistics

ISBN:

9781319013387

Author:

David S. Moore, George P. McCabe, Bruce A. Craig

Publisher:

W. H. Freeman

SEE MORE TEXTBOOKS