Question #26 Might we be able to predict life expectancies from birthrates?Below are bivariate data giving birthrate and life expectancy information for each of twelve countries. For each of the countries, both x, the number of births perone thousand people in the population, and y, the female life expectancy (in years), are given. Also shown are the scatter plot for the data and the least-squaresregression line. The equation for this line is y = 82.85 –0.48x.Birthrate, x(number of births per 1000 people)Female life expectancy, y(in years)48.063.185-16.874.080-51.354.275+24.875.47044.558.96530.964.160-41.764.255+35.666.75014.173.925.773.040só s56015.474.8Birthrate50.859.4(number of births per 1000 people)Send data to calculatorSend data to ExcelBased on the sample data and the regression line, answer the following.(a) From the regression equation, what is the predicted female life expectancy (in years) when the birthrate is 35.6 birthsFemale life expectancy(in years)回8哈 51.3Español54.224.875.444.558.930.964.141.764.235.666.70014.173.925.773.015.474.8Birthrate50.859.4(number of births per 1000 people)Send data to calculatorSend data to ExcelBased on the sample data and the regression line, answer the following.(a) From the regression equation, what is the predicted female life expectancy (in years) when the birthrate is 35.6 birthsper 1000 people? Round your answer to one or more decimal places.(b) What was the observed female life expectancy (in years) when the birthrate was 35.6 births per 1000 people?Female lire expectancy(in years)

(a) Obtain the predicted female life expectancy if the birth rate equals 35.6 births per 1000…

Answered: Might we be able to predict life…

Linear Algebra: A Modern Introduction

4th Edition

ISBN:9781285463247

Author:David Poole

Publisher:David Poole

Chapter7: Distance And Approximation

Section7.3: Least Squares Approximation

Problem 31EQ

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The following table shows the length, in centimeters, of the humerus and the total wingspan, in centimeters, of several pterosaurs, which are extinct flying reptiles. (A graphing calculator is recommended.) (a) Find the equation of the least-squares regression line for the data. (Where × is the independent variable.) Round constants to the nearest hundredth. y= ? (b) Use the equation from part (a) to determine, to the nearest centimeter, the projected wingspan of a pterosaur if its humerus is 52 centimeters. ? cm
Might we be able to predict life expectancies from birthrates?Below are bivariate data giving birthrate and life expectancy information for each of twelve countries. For each of the countries, both x, the number of births per one thousand people in the population, and y, the female life expectancy (in years), are given. Also shown are the scatter plot for the data and the least-squares regression line. The equation for this line is Ŷ=82.24-0.48x (The 2nd picture contains the rest of the data as it would not fit in the first pic and it includes the question as well.) .
A year-long fitness center study sought to determine if there is a relationship between the amount of muscle mass gained y(kilograms) and the weekly time spent working out under the guidance of a trainer x(minutes). The resulting least-squares regression line for the study is y=2.04 + 0.12x A) predictions using this equation will be fairly good since about 95% of the variation in muscle mass can be explained by the linear relationship with time spent working out. B)Predictions using this equation will be faily good since about 90.25% of the variation in muscle mass can be explained by the linear relationship with time spent working out C)Predictions using this equation will be fairly poor since only about 95% of the variation in muscle mass can be explained by the linear relationship with time spent working out D) Predictions using this equation will be fairly poor since only about 90.25% of the variation in muscle mass can be explained by the linear relationship with time spent…
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 2001 . The variable x denotes the player payroll (in millions of dollars) for the year 2001 , 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 Figure 1 scatter plot, as is the least-squares regression line. The equation for this line is =y+13.820.23x . Player payroll, x(in $1,000,000s) Mean attendance, y(in thousands) Anaheim 46.6 24.69 Baltimore 73.4 38.15 Boston 109.6 32.47 Chicago White Sox 62.4 21.85 Cleveland 92.0 39.26 Detroit 49.8 23.70 Kansas City 35.6 19.01 Minnesota 24.4 21.98 New York Yankees 109.8 40.25 Oakland 33.8 26.54 Seattle 75.7 43.33 Tampa Bay 55.0 16.05 Texas 88.5 34.94 Toronto 75.8 23.70 y 5 10 15 20 25 30 35 40 45 x 20 40 60 80 100 120 140 0…

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