### Analysis of Licensed Drivers and Fatal Accidents by Age Group and Gender#### Data SummaryThe data represents the number of licensed drivers in various age groups and the number of fatal accidents within the age group by gender. The goal is to analyze this data and determine relationships via least-squares regression lines.We will be:1. Finding the least-squares regression line for males, treating the number of licensed drivers as the explanatory variable (x) and the number of fatal crashes as the response variable (y).2. Repeating this procedure for females.#### Part (a): Least-Squares Regression Line for Males**Objective:** To find the least-squares regression line for males.**Steps to Complete:**1. Use the provided data table (available by clicking the icon) to find the necessary statistical values.2. Compute the regression line using the formula $ \hat{y} = mx + b $: - $ m $ is the slope of the line. - $ b $ is the y-intercept.3. Round the x coefficient to three decimal places.4. Round the constant (y-intercept) to the nearest integer.***Formula for the Least-Squares Regression Line for Males:***\[ \hat{y} = [X]\text{(rounded to three decimal places)} + [Y]\text{(rounded to the nearest integer)} \]This process will help in understanding the relationship between the number of licensed drivers and the number of fatal crashes for males in different age groups. #### Next Steps:After determining the regression line for males, repeat the same process for females to compare the relationships between genders.---Note: Ensure to click on the data table icon to view the data necessary for calculations. **Data for Licensed Drivers by Age and Gender**The following table provides detailed statistics on the number of licensed drivers and the number of fatal crashes, categorized by age and gender.| Age | Number of Male Licensed Drivers (000s) | Number of Fatal Crashes (Male) | Number of Female Licensed Drivers (000s) | Number of Fatal Crashes (Female) ||---------|----------------------------------------|-------------------------------|------------------------------------------|-----------------------------------|| < 16 | 12 | 227 | 12 | 77 || 16-20 | 6,424 | 5,180 | 6,139 | 2,113 || 21-24 | 6,935 | 5,016 | 6,816 | 1,543 || 25-34 | 14,203 | 8,538 | 14,664 | 2,780 || 35-44 | 20,406 | 7,990 | 20,082 | 2,742 || 45-54 | 19,898 | 7,112 | 19,984 | 2,285 || 55-64 | 14,328 | 4,527 | 14,441 | 1,514 || 65-74 | 8,194 | 2,274 | 8,377 | 938 || > 74 | 4,803 | 2,022 | 5,375 | 964 |### Explanation:- **Column 1 (Age):** Lists age groups.- **Column 2 (Number of Male Licensed Drivers in 000s):** Reflects the number of male licensed drivers in thousands.- **Column 3 (Number of Fatal Crashes (Male)):** Indicates the number of fatal crashes involving male drivers.- **Column 4 (Number of Female Licensed Drivers in 000s):** Indicates the number of female licensed drivers in thousands.- **Column 5 (Number of Fatal Crashes (Female)):** Displays the number of fatal crashes involving female drivers.### Graphs or Diagrams:No graphs or diagrams are included in this image. The table provides a comparative

The least-square regression line for males, x y x2 y2 x⋅y 12 227 144 51529 2724 6424 5180…

Answered: The data in the table represent the…

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 data show the number of felony convictions, in hundreds, and the crime rate, in crimes per 100,000, for seven randomly selected states. For the given data, a. determine the correlation coefficient between the number of felony convictions and the crime rate, (b) find the equation of the regression line, (c) approximate what crime rate can we anticipate in a state that has 12 hundred felony convictions.Felony convictions: 11.4, 8.1, 6.7, 3.4, 2.4, 2.3, 0.4Crime rate/ 100,000: 12, 9.5, 10.4, 9, 4.2, 5.5, 3.3 A. a. r = 0.906 b. y = 3.85x + 0.777 c. 47 B. a. r = 0.906 b. y = 0.777x + 3.85 c. 13.2 C. a. r = -0.906 b. y = 0.777x - 3.85 c. 5.5 D. a. r = -0.906 b. y = -0.777x + 3.85 c. 5.5
Listed below are paired data consisting of movie budget amounts and the amounts that the movies grossed. Find the regression equation, letting the budget be the predictor (x) variable. Find the best predicted amount that a movie will gross if it’s budget is $135 million. Use a significance level of 0.05 Budget ($) in Millions 44 24 112 69 71 48 121 69 4 62 125 20 11 148 3 Gross ($) in Millions 118 19 112 59 122 108 101 110 52 113 221 43 13 298 46
Read the following description of a data set. The owner of a bicycle store wonders if the employees who take the longest lunch breaks also make the fewest bicycle sales. One day, she notes the length of each salesperson's lunch break (in minutes), x, as well as the number of bicycles he or she sold that day, y. The least squares regression line of this data set is: ŷ = -0.297x + 10.889 Complete the following sentence: If a salesperson spends one additional minute at lunch, the least squares regression line predicts he or she will sell | fewer bicycles that day. Submit е
The data in the table represent the number of licensed drivers in various age groups and the number of fatal accidents within the age group by gender. Complete parts (a) through (c) below. Click the icon to view the data table. ..... (a) Find the least-squares regression line for males treating the number of licensed drivers as the explanatory variable, x, and the number of fatal crashes, y, as the response variable. Repeat this procedure for females. Find the least-squares regression line for males. y =x+O %D/ (Round the x coefficient to three decimal places as needed. Round the constant to the nearest integer as needed.) Find the least-squares regression line for females. y = ý =x+O %3D (Round the x coefficient to three decimal places as needed. Round the constant to the nearest integer as needed.) (b) Interpret the slope of the least-squares regression line for each gender, if appropriate. How might an insurance company use this information? What is the correct interpretation of the…
onsider the following sample of production volumes and total cost data for a manufacturing operation. Production Volume(units) Total Cost($) 400 3,900 450 4,900 550 5,500 600 5,900 700 6,400 750 7,100 This data was used to develop an estimated regression equation, ŷ = 970.67 + 8.08x, relating production volume and cost for a particular manufacturing operation. Use ? = 0.05 to test whether the production volume is significantly related to the total cost. (Use the F test.) Set up the ANOVA table. (Round your p-value to three decimal places and all other values to two decimal places.) Sourceof Variation Sumof Squares Degreesof Freedom MeanSquare F p-value Regression Error Total Find the value of the test statistic. (Round your answer to two decimal places.) Find the p-value. (Round your answer to three decimal places.) p-value = What is your conclusion? Reject H0. We conclude that the relationship between production…
The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 58 inches. Is the result close to the actual weight of 572 pounds? Use a significance level of 0.05. Chest size (inches) 46 57 53 41 40 40 Weight (pounds) 384 580 542 358 306 320 LOADING... Click the icon to view the critical values of the Pearson correlation coefficient r. What is the regression equation? y=nothing+nothingx (Round to one decimal place as needed.)
A. run a simple regression- dependent variable is Weeks, independent variable is Age. B. run a multiple regression with dependent variable weeks and independent variable-age, married, head, manager and sales. C. Create the regular and standardized residual plots for both. Please show the tables when entering values of the regression for both the outputs and the scatter plots.
you take a bar of soap and weigh it after each shower. will the regression line be positive or negative
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…
Listed below are paired data consisting of movie budget amounts and the amounts that the movies grossed. Find the regression equation, letting the budget be the predictor (x) variable. Find the best predicted amount that a movie will gross if its budget is $110 million. Use a significance level of alpha equals 0.05 . Budget left parenthesis $ right parenthesis in Millions 42 24 119 69 77 48 121 62 5 63 129 21 12 154 4 Gross left parenthesis $ right parenthesis in Millions 117 16 112 66 111 111 104 90 62 104 219 27 20 288 42 The regression equation is ŷ = __ + __x. (Round to one decimal place as needed.) The linear coefficient of correlation r = ___ (Round to three decimal places as needed.) The critical value of r = ____ The best predicted gross for a move with a $110 million budget is _____.
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…
Pa.n.n

SEE MORE QUESTIONS

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