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) to (c) below.Click the icon to view the data table.C...(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 femaleFind the least-squares regression line for males.ŷ=0x+0(Round the slope to three decimal places and round the constant to the nearest integer as needed.)Data for licensed drivers by age and gender.21-2425-3435-4445-5455-6465-74> 74Number of Male FatalLicensedAge Drivers (000s)< 161216-206,4246,91418,06820,406Number ofNumber of Female FatalCrashes Licensed(Males) Drivers (000s)227126,139Crashes(Females)772,1131,5345,1805,0166,8168,56717,6642,7807,99020,0472,74219,98414,4418,3865,37519,89814,3288,1944,803Number of7,1214,5272,2742,0222,2851,514938970-DXk answer

The given data is as follows:AgeNo. of male Licensed Drivers (000s)No. of Fatal crashes (Males)No.…

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

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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 $115 million. Use a significance level of a = 0.05. Budget ($)in Millions Gross ($) in Millions 41 21 114 68 79 51 118 65 9. 61 122 21 15 151 110 119 11 107 56 128 116 101 107 55 105 222 39 22 283 45 Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y%= X. %3D (Round to one decimal place as needed.)
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.)
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…
Can movie rental revenue be predicted? A movie studio wishes to determine the relationship between the revenue from rental of comedies on streaming services and the revenue generated from the theatrical release of such movies. The studio has the following bivariate data from a sample of fifteen comedies released over the past five years. These data give the revenue x from theatrical release (in millions of dollars) and the revenue y from streaming service rentals (in millions of dollars) for each of the fifteen movies. Also shown are the scatter plot and the least-squares regression line for the data. The equation for this line is ŷ=3.38+0.15x. Theater revenue, x (in millions of dollars) Rental revenue, y (in millions of dollars) 21.0 5.5 60.9 10.0 61.0 16.0 27.5 3.1 36.7 12.7 30.6 5.7 14.8 2.0 49.6 15.7 13.1 10.2 25.9 8.9 44.1 6.5 66.9 9.5 27.5 11.8 24.9 7.9 6.9 1.5 Send data to calculator Send data to Excel Rental revenue (in millions of dollars) 18- 16+ x 14 12 10+ x 50 60 70…
For major league baseball teams, do higher player payrolls mean more gate money? Here are data for each of the American League teams in the year 2002. The variable x denotes the player payroll (in millions of dollars) for the year 2002, and the variable y denotes the mean attendance (in thousands of fans) for the 81 home games that year. The data are plotted in the scatter plot below, as is the least-squares regression line. The equation for this line is y = 11.43 + 0.23x. Player payroll, x (in Mean attendance, y (in $1,000,000s) thousands) Anaheim 62.8 28.52 Baltimore 56.5 33.09 40- Boston 110.2 32.72 35 Chicago White Sox 54.5 20.74 30- Cleveland 74.9 32.35 25- Detroit 54.4 18.52 Kansas City 49.4 16.30 15- Minnesota 41.3 23.70 10+ New York Yankees 133.4 42.84 Oakland 41.9 26.79 20 40 60 80 100 120 140 Seattle 86.1 43.70 Player payroll, Тarmpa Bay 34.7 13.21 X (in $1,000,000s) Техas 106.9 29.01 Toronto 66.8 20.25 Send data to calculator Send data to Excel Based on the sample data and…
A movie studio wishes to determine the relationship between the revenue from rental of comedies on streaming services and the revenue generated from the theatrical release of such movies. The studio has the following bivariate data from a sample of fifteen comedies released over the past five years. These data give the revenue x from theatrical release (in millions of dollars) and the revenue y from streaming service rentals (in millions of dollars) for each of the fifteen movies. Also shown are the scatter plot and the least-squares regression line for the data. The equation for this line is y = 3.58 +0.15x. Theater revenue, x (in millions of dollars) 26.4 36.7 43.5 31.6 60.4 14.8 21.3 49.3 7.7 24.8 27.8 12.9 60.6 26.4 66.4 Send data to calculator V Rental revenue, y (in millions of dollars) 8.1 12.4 6.8 5.1 15.4 2.6 5.8 16.5 2.2 9.1 3.3 9.8 10.8 11.7 9.6 Based on the studio's data and the regression line, complete the following. Rental revenue in millions of dollars) 0 X 10 X 20…
We have data from 209 publicly traded companies (circa 2010) indicating sales and compensation information at the firm-level. We are interested in predicting a company's sales based on the CEO's salary. The variable sales; represents firm i's annual sales in millions of dollars. The variable salary; represents the salary of a firm i's CEO in thousands of dollars. We use least-squares to estimate the linear regression sales; = a + ßsalary; + ei and get the following regression results: . regress sales salary Source Model Residual Total sales salary cons SS 337920405 2.3180e+10 2.3518e+10 df 1 207 208 Coef. Std. Err. .9287785 .5346574 5733.917 1002.477 MS 337920405 111980203 113066454 Number of obs F (1, 207) Prob > F R-squared t P>|t| = Adj R-squared = Root MSE 1.74 0.084 5.72 0.000 = = -.1252934 3757.543 = 209 3.02 0.0838 0.0144 0.0096 10582 [95% Conf. Interval] 1.98285 7710.291 This output tells us the regression line equation is sales = 5,733.917 +0.9287785 salary. Interpret the…
The accompanying table shows a portion of data that refers to the property taxes owed by a homeowner (in $) and the size of the home (in square feet) in an affluent suburb 30 miles outside New York City. Click here for the Excel Data File Taxes Size 21,987 2,403 17,353 2,451 29,238 2,866 a. Estimate the sample regression equation that enables us to predict property Taxes on the basis of the size of the home. (Round your answers to 2 decimal places.) Taxes = + Size. b. Interpret the slope coefficient. O As Property Taxes increase by 1 dollar, the size of the house increases by 6.71 ft. O As Size increases by 1 square foot, the property taxes are predicted to increase by $6.71. c. Predict the property Taxes for a 1,400-square-foot home. (Round coefficient estimates to at least 4 decimal places and final answer to 2 decimal places.) Taxes
You are given the following data, where X1X1 (final percentage in science class) and X2X2 (number of absences) are used to predict YY (standardized science test score in third grade): X1X1 X2X2 YY 95 2 415 82 0 400 92 6 375 72 1 410 75 4 370 80 1 390 78 3 350 70 3 345 65 7 300 70 3 350 88 3 375 89 1 450 Determine the following multiple regression values.Report intercept and slopes for regression equation accurate to at least 2 decimal places: Intercept: b0=b0= Partial slope X1X1: b1=b1= Partial slope X2X2: b2=b2= Report the coefficient of multiple determination for the model (NOT the adjusted R2R2) and the sum of squares total accurate to at least 2 decimal places: R2=R2= SSTotal=SSTotal= Test the significance of the overall regression model. F-test statistic = P-value = Report the Mean Squares residuals accurate to 2 decimal places: Report the test statistics for the regression coefficients accurate to 2 decimal places:…

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