15) After declining over the past century, the percentage of men aged 65 and older in the workforce hasbegun to rise in recent years, as shown by the following tableYearPercent of men 65 orolder in the workforce192055.6193054194041.8195045.8096197033.126.8198016.306000,201017.720.5202023.3A) use the regression feature on your calculator to find a cubic function (use CubicReg) that models thedata LET x=0 correspond to the year 1920 and change all the other years accordinglyb) Use your answer in part a to the rate the at which the percent of men aged 65 and older in theworkforce was changing in 1965, 2015 and 2021punn

As per the question we are given th following data table : And we have to find the best fit cubic…

Answered: 15) After declining over the past…

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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Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
1) What is the probability of a stroke over the next 10 years for John Smith, a 68-year-old smoker who has blood pressure of 175? What action might the physician recommend for John to reduce the risk of stroke? 2) Is there any multicollinearity problem in the above multiple regression model? How do you know?
If the standard error of the estimate for a regression model fitted to a large number of paired observations is 1.75, approximately 95% of the residuals would lie within ______. −3.50 and +3.50 −1.75 and +1.75 −0.95 and +0.95 −0.68 and +0.68 −0.97 and +0.97
In the following model, "employed" is a dummy indicating a person is employed: donation = B + B edu + Bemployed + uT Running this model will produce the same results of differential in donation between employed people and unemployed people as running two separate regressions for employed people and unemployed people. A. True B. False
A researcher notes that, in a certain region, a disproportionate number of software millionaires were born around the year 1955. Is this a coincidence, or does birth year matter when gauging whether a software founder will besuccessful? The researcher investigated this question by analyzing the data shown in the accompanying table. Complete parts a through c below. a. Find the coefficient of determination for the simple linear regression model relating number (y) of software millionaire birthdays in a decade to total number (x) of births in the region. Interpret the result. The coefficient of determination is 1.___? (Round to three decimal places as needed.) This value indicates that 2.____ of the sample variation in the number of software millionaire birthdays is explained by the linear relationship with the total number of births in the region. (Round to one decimal place as needed.) b. Find the coefficient of determination for the simple linear regression model…
The following data shows the percent of females 18 years old or older who were overweight in the number of years indicated, judging on the basis of BMI. Year, Percent Obese 3 , 23.3 4, 23.7 5, 24.3 6, 25.6 7, 25.2 8, 27.6 9, 26.8 Using regression equation, predict the percent of overweight females in year 15.
In Australia, 16% of the adult population is nearsighted.17 If three Australians are chosen at random, what is the probability that two are nearsighted and one is not? 2.state each of the ﬁve assumptions of the classical regression model (OLS) and give an intuitive explanation of the meaning and need for each of them.
The following results are from data concerning the amount withdrawn from an ATM machine based on the amount of time spent at the ATM machine (SECONDS) and the gender, FEMALE (dummy variable = 1 for females and = 0 for males) and an interaction term, SECONDS*FEMALE Based on the regression results, if a male and female each spend the same amount of time at the ATM machine (say 27 seconds), how much more (or less) will a male withdraw? (if a male withdraws more then your answer should be a positive number and if a male withdraws less then your answer should be a negative number? (please express your answer using 1 decimal places)
Given the estimated least square regression line y=2.48+1.63x, and the coefficient of determination of 0.81, What is the value of correlation coefficient?

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