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=82.25 -0.48x.^Birthrate, x(number of births per 1000 people)35.544.929.719.913.727.051.915.050.949.739.624.4Send data to calculatorSend data to ExcelFemale life expectancy, y(in years)67.957.961.771.472.573.555.476.558.260.664.474.4Based on the sample data and the regression line, complete the following.Female life expectancy(in years)8580+75+70-65+60-55+50xXx++10 15 20 25(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?0x30(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.*3+XXBirthrate(number of births per 1000 people)40 45 50 55 60XŚEsp

Given the regression equation : y^=82.25-0.48x

Answered: Based on the sample data and the…

A First Course in Probability (10th Edition)

10th Edition

ISBN:9780134753119

Author:Sheldon Ross

Publisher:Sheldon Ross

Chapter1: Combinatorial Analysis

Section: Chapter Questions

Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...

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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 date and the least-squares regression line. The equation for this line is y=82.84-0.49x. Birthrate, x (number of births per 1000 people) 24.6 50.1 40,6 20.0 50.7 28.1 143 49.0 31.9 14.8 33.6 46.5 Send data to calculator Send data to Excel Female life expectancy, y (in years) 74.3 54.4 66.2 73.6 58.9 72.7 76.1 61.5 62.4 71.4 68.0 57:5 Based 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 analyst's data and regression line, complete the following. For these data, values for earnings per share that are greater than the mean of the values for earnings per share tend to be paired with current stock prices that are (greater than, lesser than) the mean of the current stock prices. According to the regression equation, for an increase of one dollar in earnings per share, there is a corresponding increase of how many dollars in current stock price?
What is the differed annual expenditures of two families if their annual net incomes are differed by 2000? The computed regression line has a value of a=4.32 and b=2.12.
Suppose the estimaited OLS regression is: Happiness = a + b*dailychocolates Now use chocolate consumprion per week instead of days. What is the relationship between the old and new units? How would this affect b (i.e. what is bnew in terms of b)?
4:42 ul LTE AA www-awn.aleks.com O REGRESSION AND CORRELATION Predictions from the least-square. The well-known psychologist Dr. Elbod has established what he calls his Generalized Anxiety Scale (GAS). The GAS, which is a scale from o to 10, measures the "general anxiety" of an individual, with higher GAS scores corresponding to more anxiety. (Dr. Elbod's assessment of anxiety is based on a variety of measurements, both physiological and psychological.) We're interested in making predictions about individuals' sleep behavior based on their GAS scores. The bivariate data below give the GAS score (denoted by x) and the number of hours of sleep last night (denoted by y ) for each of the fifteen adults in a study. The least-squares regression line for these data has equation =8.36-0.25x. This line, along with a scatter plot of the sample data, is shown in Figure 1. Sleep time, y (in hours) GAS score, x 6.0 8.1 6.5 5.9 10- 5.1 6.0 7.9 5.7 7.1 6.7 1.0 7.4 8.1 7.2 3.0 6.9 2.1 8.1 9.0 5.8 9.2…
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…
The Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of sixteen Cadets, each bought new two years ago, and each sold used within the past month. For each Cadet in the sample, we have listed both the mileage x (in thousands of miles) that the Cadet had on its odometer at the time it was sold used and the price y (in thousands of dollars) at which the Cadet was sold used. With the aim of predicting the used selling price from the number of miles driven, we might examine the least-squares regression line, y=41.57 – 0.49.x. This line is shown in the scatter plot in Figure 1. Used selling price, Mileage, x (in thousands) (in thousands of dollars) 25.9 26.1 28.1 26.2 40- 21.1 31.4 24.0 27.5 35 27.2 30.9 38.7 21.4 30. 34.6 25.5 37.2 23.5 15.6 34.0 25- 23.8 28.0 20.9 30.9 20. 23.1 32.7 28.0 30.3 40 29.2 28.1 Figure 1 24.0 29.6 23.0 31.5 Send data to Excel
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 datasetBody.xlsgives the percent of weight made up of body fat for 100 men as well as other variables such as Age, Weight (lb), Height (in), and circumference (cm) measurements for the Neck, Chest, Abdomen, Ankle, Biceps, and Wrist. We are interested in predicting body fat based on abdomen circumference. Find the equation of the regression line relating to body fat and abdomen circumference. Make a scatter-plot with a regression line. What body fat percent does the line predict for a person with an abdomen circumference of 110 cm? One of the men in the study had an abdomen circumference of 92.4 cm and a body fat of 22.5 percent. Find the residual that corresponds to this observation. Bodyfat Abdomen 32.3 115.6 22.5 92.4 22 86 12.3 85.2 20.5 95.6 22.6 100 28.7 103.1 21.3 89.6 29.9 110.3 21.3 100.5 29.9 100.5 20.4 98.9 16.9 90.3 14.7 83.3 10.8 73.7 26.7 94.9 11.3 86.7 18.1 87.5 8.8 82.8 11.8 83.3 11 83.6 14.9 87 31.9 108.5 17.3…
Dr. Lillian Fok, a New Orleans psychologist, specializes in treating patients who are agoraphobic (i.e., afraid to leave their homes). The following table indicates how many patients Dr. Fok has seen each year for the past 10 years. It also indicates what the robbery rate was in New Orleans during the same year. Year Number of Patients Robbery Rate per 1,000 Population The simple linear regression equation that shows the best relationship between the number of patients and the robbery rate is (round your responses to three decimal places) where y Number of Patients and x = Robbery Rate. = 1 2 3 4 6 7 36 33 40 41 55 60 58.3 61.1 73.4 75.7 81.1 89.0 101.1 5 40 8 54 94.8 9 58 103.3 10 61 116.2
Consider the following passage: I ran a regression, with many variables to predict the result of another variable which was the murder rate. One can see that lots of things, can cause the murder rate to increase or decrease. I tried to account for all the important factors, and those factors are the SAT scores, unemployment rate, and international migration per 1,000. The SAT score is, average combined total score participants did on the SAT exam. The unemployment rate is, "a measure of the prevalence of unemployment and it is calculated as a percentage by dividing the number of unemployed individuals by all individuals currently in the labor force." (Wikipedia) International migration per 1,000 is, the number of people who come into a state from other countries per 1,000 people who live in the state. After I run the regression I will look at the t scores and p values and I should hopefully conclude that international migration does not cause crime. Which writing mistakes, if any, did…
please only answer the last part "Suppose that real income per capita in New Jersey increases by 1% in the next year. Use the results in column (4) to predict the change in the number of traffic fatalities in the next year. "

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