A data set is given below.(a) Draw a scatter diagram. Comment on the type of relation that appears to exist between x and y.(b) Given that x= 3.3333, s= 2.5033, y = 4.0667, s, = 1.9449, and r= - 0.9585, determine the least-squares regression line.(c) Graph the least-squares regression line on the scatter diagram drawn in part (a).345.8 6.3 5.1 3.4 1.8 2.0重(a) Choose the correct graph below.OA.O B.OD.本y7-Ay本y7-AY7-0-0.0-0.6.There appears to berelationship.(b) ŷ =[]x+ (D(Round to three decimal places as needed.)(c) Choose the correct graph below.O A.О В.OC.O D.Ay7-Ay7-0-0.9.6.O Time Remaining:DTCOA2334403U 10-8-2014MAINTCOA2330401(2)Q出

(a) Use EXCEL to construct the scatter plot. EXCEL procedure: Go to EXCEL Go to Insert menu…

Answered: Adata set is given below. (a) Draw 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

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An engineer wants to determine how the weight of a gas-powered car, x, affects gas mileage, y. The accompanying data represent the weights of various domestic cars and their miles per gallon in the city for the most recent model year. Complete parts (a) through (d) below. (a) Find the least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable. y=nothingx+(nothing) (Round the x coefficient to five decimal places as needed. Round the constant to one decimal place as needed.) (b) Interpret the slope and y-intercept, if appropriate. Choose the correct answer below and fill in any answer boxes in your choice. (Use the answer from part a to find this answer.) A. A weightless car will get nothing miles per gallon, on average. It is not appropriate to interpret the slope. B. For every pound added to the weight of the car, gas mileage in the city will decrease by nothing mile(s) per gallon, on…
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 where x is metatarsal-to-femur ratio and y 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 a 96% confidence interval for the slope of the population regression line. Give your answers precise to at least two decimal places. contact us help 6:42 PM povecy polcy terms of use careers A E O 4») 18 -క90.4 58 12/14/2020 a 17 |耳即 delets prt sc insert 112 19 18 + 16 backspace f5 fA
(b)
Cathy makes a single deposit into an account earning 2.5% compounded monthly. After five years the account contains $3057.46.how much did she deposit?
3 of 14 For many people, the women's figure skating competition is the highlight of the Olympic Winter Games. Scores in the short program x and scores in the free skate y were recorded for each of the 24 skaters who competed in both rounds during the 2010 Winter Olympics in Vancouver, Canada. Here is a scatterplot with least-squares regression line y =-16.2 +2.07x. For this model, s = 10.2 and = 0.736. 160 Interpret the value of s. 140 O 10.2% of the variation in free skate score is 120 accounted for by the least-squares regression line with x = short program score. The actual free skate score is typically about 10.2 points lower than the score predicted by the least- squares regression line with x = short program score. O For every 1 point increase in the short program score, the predicted free skate score increases by 10.2 points. O The actual free skate score is typically about 10.2 points higher than the score predicted by the least- squares regression line with x = short program…
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…
1.)fill in the blanks. Based on the physician's study, the predictor variable, X is_____and the response variable, Y, is________. 2.) As described in the article, the relation between age and peak heart is a______. - positive relation - negative relation - no relation 3.) Provide the regression line, ŷ=a+bx. Show steps/equations used to get answer. 4.) Suppose a 40 year old person is randomely selected. Use your Model to predict their peak heart rate. 5.) Based on your model, as a person ages one year, how much would you expect peak heart rate to change?
A sample of 50 women is obtained, and their heights (in inches) and pulse rates (in beats per minute) are measured. The linear correlation coefficient is 0.245 and the equation of the regression line is y=17.6 + 0.910x,where x represents height. The mean of the 50 heights is 62.9 in and the mean of the 50 pulse rates is 72.3 beats per minute. Find the best predicted pulse rate of a woman who is 67 in tall. Use a significance level of (alpha equals) α=0.05.
An engineer wants to determine how the weight of a gas-powered car, x, affects gas mileage, y. The accompanying data represent the weights of various domestic cars and their miles per gallon in the city for the most recent model year. Complete parts (a) through (d) below. Click here to view the weight and gas mileage data. (a) Find the least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable. Car Weight and MPG (Round the x coefficient to five decimal places as needed. Round the constant to two decimal places as needed.) Weight (pounds), x Miles per Gallon, y 3806 16 3796 15 2669 24 3520 18 3361 21 2911 22 3808 18 2612 24 3375 19 3737 16 3320 19
Owners of Major League Baseball teams have been concerned with attendance in recent years as declines have been noticed across the board. There are a variety of factors that are causing this decline, but it is believed that a team's win-loss record, payroll, stadium age, and home run ability help to drive attendance. space What was the average attendance per team in 2018? Round your answer to 3 decimal places. SPACE What team had the highest and lowest total attendance in 2018? Enter the team names along with their attendance figures as they appear in the data file. Round your attendance numbers to 3 decimal places. SPACE Team Attendance Highest attendance mil Lowest attendance mil space What was the range of attendance in 2018? Round your answer to 3 decimal places.
A sample of 70mwomen is obtained, and their heights (in inches) and pulse rates (in beats per minute) are measured. The linear correlation coefficient is 0.255and the equation of the regression line is y(caret) 17.8 + 0.950 x where x represents height. The mean of the 70 heights is 62.7in and the mean of the 70 pulse rates is74.5 beats per minute. Find the best predicted pulse rate of a woman who is 68 in tall. Use a significance level of alpha equals 0.01
FRQ 2 Professional basketball teams have 11 players per team. Salary is dependent upon their scoring average, measured in points per game. A least-squares regression line that describes the relationship between scoring average and salary for one professional basketball team is ŷ = 2,671,134.68 +684,663.08x, where x is the player's scoring average and y is the player's salary. The residuals for this regression are given in the graph below. Residual $20,000,000 $15,000,000 $10,000,000 $5,000,000 $0 -$5,000,000 -$10,000,000 -$15,000,000 4 ITS % 6 MacBook Pro ● 8 ● ● ● 10 12 14 Scoring Average (Points per Game) Is a line an appropriate model to use for these data? What information tells you this? b) What is the value of the slope of the least-squares regression line? Interpret the slope in the context of this problem. c) What is the predicted salary of the basketball player with 10.9 points per game? d) Approximate the actual salary of the basketball player with 10.9 points per game. 16 tv…

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