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=83.86-0.52x.Birthrate, xFemale life expectancy, y(in years)(number of births per 1000 people)34.467.319.672.949.258.8XX39.463.828.073.350.353.914.177.315.573.345.057.830.863.250.661.826.073.3Send data to calculator VBased 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 30.8 births per1000 people? Round your answer to one or more decimal places.(b) From the regression equation, what is the predicted female life expectancy (in years) when the birthrate is 23.6 births per1000 people? Round your answer to one or more decimal places.0Female life expectancy(In years)X40 4550 55Birthrate(number of births per 1000 people)

a) In this case, the birthdate is the independent variable (x) and the female life expectancy is the…

Answered: Might we be able to predict life…

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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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 Figure 1 scatter plot, as is the least-squares regression line. The equation for this line is î=11.43 +0.23x. Player payroll, x (in $1,000,000.0s) Mean attendance, y (in thousands) Anaheim 62.8 28.52 Baltimore 56.5 33.09 Boston 110.2 32.72 40- Chicago White 35- 54.5 20.74 Sox 30 Cleveland 74.9 32.35 25 Detroit 54.4 18.52 20 Kansas City 49.4 16.30 Minnesota 41.3 23.70 15 New York Yankees 133.4 42.84 10 Oakland 41.9 26.79 5 Seattle 86.1 43.70 100 120 140 Tampa Bay 34.7 13.21 Texas 106.9 29.01 Figure 1 Toronto 66.8 20.25
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The table below shows the heights (in feet) and the number of stories of six notable buildings in a city. (b) x = 641 feet (d) x = 725 feet (a) x = 500 feet (c) x = 345 feet 483 492 510 518 621 Height, x Stories, y 758 37 35 41 44 47 51 Find the regression equation. ŷ=x+ (O Q. (Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed.) th Choose the correct graph below. 12 OD. OC. O B. OA. 60- 60- 60- 60- cion 0- ons 800 800 800 800 Height (feet) Height (feet) Height (feet) Height (feet) Quest (a) Predict the value of y for x = 500. Choose the correct answer below. ed Que O A. 51 d Quest O B. 39 O C. 47 Click to select your answer(s). tv DEC MacBook…
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
A sports blogger is looking to determine an equation that relates a professional football quarterback's salary with his passer rating, touchdowns thrown, the average number of wins per season, and the number of years the quarterback has been in the league. What is the correct format for a multiple regression equation? Select the correct answer below: yˆ=b0+b1x1+b2x2+b3x3 yˆ=b1x1+b2x2+b3x3 yˆ=b0+b1x1+b2x2+b3x3+b4x4 yˆ=b0+b1x1+b2x2
A researcher collected statistics on the sales amount of a product in 120 different markets and the advertising budgets used in TV, radio and newspaper media channels for each of these markets. The sales amount are expressed in 1000 units, and the budgets are expressed in 1000$. The researcher wants to create a simple linear regression model by choosing one among the TV, radio and newspaper advertising budgets to explain the amount of sales. Accordingly, answer the following question by using the data in the "Regression Data Set" document in the appendix. 1) a) In your opinion, which variable should this researcher choose as an independent variable to the simple regression model? Explain your decision by providing its statistical basis.
The average gestational age (time from conception to birth) of a newborn infant is about 40 weeks. The following data presents the gestational age in weeks and corresponding mean birth weight in pounds for female infants born in Canada. Find the least-squares regression line for predicting the birth weight from the gestational age. You should be able to grab the data in the table, copy it, and paste it in Excel. You would then select the data in Excel, Insert a Scatter Chart and then under Trendline, check the two boxes at the bottom to display the equation and the R-squared. Gestational Age Birth Weight 36 6.1 37 6.6 38 7.0 39 7.4 40 7.7 41 7.9 42 8.0 43 8.1 A. y= 3.2787x + 15.402 B. y= 15.402x + 3.2787 C. y= 0.2857x - 3.9357 D. Y= 3.9357X + 0.2857
Please help me understand this problem more in depth. A researcher is investigating possible explanations for deaths in traffic accidents. He examined data from 2000 for each of the 52 cities randomly selected in the US. The data included information on the following variables: Deaths: The number of deaths in traffic accidents per city Income: The median income per city As part of his study, he ran the following simple linear regression model attached in photo. Question: Based on the above results, the researcher tested the hypotheses: Ho: B1=0 versus B1 not equal to 0, versus using T test. What do we know about the test statistic of the test? Based on the approximate p-value, what's the conclusion?
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 y = 81.87– 0.46x. Birthrate, x (number of births per 1000 рop.) Female life expectancy, y (in years) 85- 35.7 67.7 80 41.5 63.9 31.9 63.3 75+ メメ 19.9 73.0 70- 50.5 60.4 65- 24.4 72.7 60- 50.1 63.2 55 13.8 72.5 50 50.3 54.6 45.6 57.9 15.9 76.2 Figure 1 26.6 71.9
It is believed that the electrical energy that a chemical plant consumes each month is related to the average environmental temperature, the number of days in the month, the average purity of the product, and the tons of product manufactured. Historical data is available for the previous year and is presented in the following table (seen in the image=. Perform simple linear regression analysis
Use the p-value criterion to find the best model for predicting the number of points scored per game by football teams using the accompanying National Football League Data. Does the model make logical sense? Click the icon to view the National Football Leaque Data. Determine the best multiple regression model. Let X, represent Rushing Yards, let X, represent Passing Yards, let X, represent Penalties, let X, represent Interceptions, and let Xg represent Fumbles. Enter the terms of the equation so that the Xy-values are in ascending numeral order by base. Select the correct choice below and fill in the answer boxes within your choice. (Type an integer or decimal rounded to three decimal places as needed.) O A. Points/Game = O B. Points/Game = Oc. Points/Game = OD. Points/Game = + ( DX Points/Game = O E.
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The table below shows the heights (in feet) and the number of stories of six notable buildings in a city. Height, x Stories, y 520 508 (a) x = 501 feet (c) x = 318 feet (b) x = 639 feet (d) x = 732 feet 766 620 46 494 484 51 45 43 39 35 Find the regression equation. (Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed.) Choose the correct graph below. OA. OB. Oc. Oc. OD. 60+ 60 60+ 50+ 0- 0- 800 800 Height (feet) 800 Height (feet) 800 Height (feet) Height (feet) (a) Predict the value of y for x = 501. Choose the correct answer below. O A. 40 O B. 50 C. 46 O D. not meaningful (b) Predict the value of y for x = 639. Choose the correct answer…
The least-squares regression equation is y=620.6x+16,624 where y is the median income and x is the percentage of 25 years and older with at least a bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7004. Interpret the slope.

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