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 date and the least-squaresregression line. The equation for this line is y=82.84-0.49x.Birthrate, x(number of births per 1000 people)24.650.140,620.050.728.114349.031.914.833.646.5Send data to calculatorSend data to ExcelFemale life expectancy, y(in years)74.354.466.273.658.972.776.161.562.471.468.057:5Based on the sample data and the regression line, complete the following.Female life expectancy(in years)Birthrate(number of births per 1000 people) Based on the sample data and the regression line, complete the following.(a) For these data, birthrates that are less than the mean of the birthrates tend to be paired with female life expectanciesthat are (Choose one) the mean of the female life expectancies.(b) According to the regression equation, for an increase of one (birth per 1000 people) in birthrate, there is acorresponding decrease of how many years in female life expectancy?0>

From the given scatterplot and equation:

Answered: Based on the sample data and 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 systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 85 mm Hg. Use a significance level of 0.05. Right Arm 100 99 91 76 76 5 Left Arm 175 170 146 147 146 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y = +x (Round to one decimal place as needed.) Given that the systolic blood pressure in the right arm is 85 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg. (Round to one decimal place as needed.)
Suppose that you are modelling cooling costs (y) as a function of the daily high temperature (x) for a building, using a simple linear regression model. You have data consisting of pairs of observations (x, y) over many years. Suppose that from this data you find a Pearson correlation between x and y of 0.5. If tomorrow the temperature is 3 standard deviations above the mean, how many standard deviations above the mean would you expect the cooling costs to be? If it is true that, tomorrow, the temperature is as given in the previous problem, then the expectation that you gave is the exact cooling cost that will occur. True O False
From a regression equation r2= 0.39 and the slope = -2.8 What is the linear correlation coefficient r?
Using the regression formula with a slope = .704 and an intercept = .719, what would the predicted college GPA be for a student whose current high school GPA = 3.2?
• In a political science class, the teacher gives a midterm exam and a final. The association between midterm and final scores is linear. The summary statistics are shown below. Midterm Mean=75, Midterm Standard Deviation=10 Final Mean=75, Final Standard Deviation=10 r=0.8 According to the regression equation, for a student who gets 85 on the midterm (one standard deviation above average) what is the predicted final exam grade? 83 85 70 75
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While attempting to measure its risk exposure for the upcoming year, an insurance company notices a trend between the age of a customer and the number of claims per year. It appears that the number of claims keep going up as customers age. After performing a regression, they find that the relationship is (claims per year) = 0.385*(age) + 5.218. If a customer is 44.315 years old and they make an average of 11.239 claims per year, the residual is -11.04. Interpret this residual in terms of the problem. Question 10 options: 1) The number of claims per year is 11.04 claims less than what we would expect. 2) The age is 11.04 years larger than what we would expect. 3) The age is 11.04 years less than what we would expect. 4) The number of claims per year is 11.239 claims less than what we would expect. 5) The number of claims per year is 11.04 claims…
Below is some of the regression output from a regression of the amount various customers paid for a new car (expressed in dollars) versus the age of the customer (expressed in years), the number of previous cars the customer had purchased from the dealership in the past, a dummy variable indicating the gender of the customer (=1 for a Man and = 0 for a woman), and an interactive term the multiplies the age of the customer with the gender dummy variable. Regression Statistics Multiple R 0.963 R Square Adjusted R Square Standard Error Observations 20 ANOVA df SS MS F Significance F Regression 24686354.49 6171589 47.8 2.3289E-08 Residual 129243 Total 26625000…
Increase in sales (percent) An advertising firm wishes to demonstrate to its clients the effectiveness of the advertising campaigns it has conducted. The following bivariate data on twelve recent campaigns, including the cost of each campaign (in millions of dollars) and the resulting percentage increase in sales following the campaign, were presented by the firm. Based on these data, we would compute the least-squares regression line to be y = 6.16+0.18x, with x representing campaign cost and y representing the resulting percentage increase in sales. (This line is shown below, along with a scatter plot of the data.) Increase in sales, y Campaign cost, x (in millions of dollars) (percent) 3.02 6.91 7.2+ 1.92 6.80 3.83 6.85 6.8- 1.40 6.37 6.6 - 3.12 6.42 3.56 6.82 6.4- 4.06 6.94 6.2 1.64 6.56 6- 2.06 6.50 1.62 6.18 1.5 2.5 3.5 6.66 Campaign cost (in millions of dollars) 2.87 2.25 6.61 Send data to calculator Based on the firm's data and the regression line, complete the following. (a)…
The accompanying data are the number of wins and the earned run averages (mean number of earned runs allowed per nine innings pitched) for eight baseball pitchers in a recent season. Find the equation of the regression line. Then construct a scatter plot of the data and draw the regression line. Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. If the x-value is not meaningful to predict the value of y, explain why not. (a) x = 5 wins Click the icon to view the table of numbers of wins and earned run average. (b) x = 10 wins (c) x = 21 wins (d) x = 15 wins ERA 6- ERA 6- AERA 6- ERA 6- 4- 4- 4- 4- 2- 2- 2- 2- 0+ 6 0- 0- 0- 12 18 24 6. 12 18 24 12 18 24 6 12 18 24 Wins Wins Wins Wins (a) Predict the ERA for 5 wins, if it is meaningful. Select the correct choice below and, if necessary, fill in the answer box within your choice. A. ŷ= (Round to two decimal places as needed.) B. It is not meaningful to predict this value of y because…
A regression between foot length (response variable in cm) and height (explanatory variable in inches) for 42 students resulted in the following regression equation: y = 10.4 + 0.21x If a student happened to be extraordinarily tall (say, 10ft, just for the fun of it) and had a foot size of 25 cm, how might this affect the slope of the regression line?
A local retail store compared their monthly sales of umbrellas with the amount of rainfall that occured during that month. They computed the following statistics: Rainfall (in) # of umbrellas mean = 4.64 mean = 34.2 SD = 1.17 SD = 13.2 r = 0.8 1. Find the equation for the regression line that predicts the monthly sales of umbrellas from monthly rainfall.

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