HeipA regression of average weekly earnings (AWE, measured in dollars) on age (measured in years) using a random sample ofcollege-educated full-time workers aged 25-65 yields the following:AWE = 647.9310 +8.9280 × Age, R2 = 0.021, SER=580.4.The coefficientshows the marginal effect of Age on AWE; that is, AWE is expected to increase by $for each additional yearof age.is the intercept of the regression line. It determines the overall level of the line.(Round your responses to four decimal places.)The standard error of the regression (SER) is 580.4. What are the units of measurement for the SER?O A. Dollars.O B. Dollars per year.C. Unit-free.O D. Dollars per week.The regression R is 0.021. What are the units of measurement for the R2?O A. Dollars per year.O B. Dollars.Click to select your answer(s).W0正e to search The regression R is 0.021. What are the units of measurement for the R2?O A. Dollars per year.OB. DollarsO C. Unit-free.O D. Dollars per week.What is the regression's predicted earnings for a 25-year-old worker?The regression's predicted earnings for a 25-year-old worker are $(Round your response to two decimal places.)Will the regression give reliable predictions for a 81-year-old worker?A. No, the oldest worker in the sample is 65 years old; 81 years is far outside the range of the sample data.OB. Yes, although the oldest worker in the sample data is 65 years old, the model is developed to make forecasts andpredictions for workers younger than 25 years of age and older than 65 years of age.Given what you know about the distribution of earnings, do you think it is plausible that the distribution of errors in the regression isnormal?O A. No, the distribution of earnings is negatively skewed and has kurtosis smaller than the normal.OB. Yes the distribution of earninas is symmetric and thus normalClick to select your answer(s).DELLsearch

Note : Since we only answer up to 3 sub-parts, we’ll answer the first 3. Please resubmit the…

A regression of average weekly earnings (AWE, measured in dollars) on age (measured in years) using a random sample of college-educated full-time workers aged 25-65 yields the following: AWE = 647.9310 + 8.9280 x Age, R2 = 0.021, SER = 580.4. %3D The coefficient shows the marginal effect of Age on AWE; that is, AWE is expected to increase by $ for each additional y of age. is the intercept of the regression line. It determines the overall level of the line. (Round your responses to four decimal places.) The standard error of the regression (SER) is 580.4. What are the units of measurement for the SER? A. Dollars. O B. Dollars per year. O C. Unit-free. D. Dollars per week.

A regression of average weekly earnings (AWE, measured in dollars) on age (measured in years) using a random sample of college-educated full-time workers aged 25-65 yields the following: AWE = 647.9310 + 8.9280 x Age, R2 = 0.021, SER = 580.4. %3D The coefficient shows the marginal effect of Age on AWE; that is, AWE is expected to increase by $ for each additional y of age. is the intercept of the regression line. It determines the overall level of the line. (Round your responses to four decimal places.) The standard error of the regression (SER) is 580.4. What are the units of measurement for the SER? A. Dollars. O B. Dollars per year. O C. Unit-free. D. Dollars per week.

College Algebra

7th Edition

ISBN:9781305115545

Author:James Stewart, Lothar Redlin, Saleem Watson

Publisher:James Stewart, Lothar Redlin, Saleem Watson

Chapter1: Equations And Graphs

Section: Chapter Questions

Problem 10T: Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s...

See similar textbooks

Similar questions

What does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?
Find the mean hourly cost when the cell phone described above is used for 240 minutes.
For the following exercises, use Table 4 which shows the percent of unemployed persons 25 years or older who are college graduates in a particular city, by year. Based on the set of data given in Table 5, calculate the regression line using a calculator or other technology tool, and determine the correlation coefficient. Round to three decimal places of accuracy
Table 6 shows the population, in thousands, of harbor seals in the Wadden Sea over the years 1997 to 2012. a. Let x represent time in years starting with x=0 for the year 1997. Let y represent the number of seals in thousands. Use logistic regression to fit a model to these data. b. Use the model to predict the seal population for the year 2020. c. To the nearest whole number, what is the limiting value of this model?
Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
Table 2 shows a recent graduate’s credit card balance each month after graduation. a. Use exponential regression to fit a model to these data. b. If spending continues at this rate, what will the graduate’s credit card debt be one year after graduating?
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?
What is regression analysis? Describe the process of performing regression analysis on a graphing utility.
According to human capital theory, a person’s earning is linked to her level of education – there is a relationship between workers income and years of education. Using data from the Labour Force Survey, a researcher found the following regression results for earnings on intercept, years of education, experience, and experience squared: Earnings = 5.24 + 0.035 educ + 0.165 exper – 0.003 exper2 (2.45) (0.012) (0.031) (0.001) Construct a 95% confidence interval for the effect of years of education on earnings ? 2. Consider an individual with 8 years of experience. What would you expect to be the return to two (2) additional years of experience (the effect on earnings)? 3. According to economic theory,…
In a regression study, relating Price/unit (x) to Weekly Sales (in Kg.), with the scatter plot showing a strong negative direction, 63% of the variability in sales could be accounted for by the variation in the Unit Price. The correlation coefficient in this study is: 0.7 -0.4 -0.79 0.4
A regression of the LDL cholesterol level on the Body Mass Index (BMI) is shown below:LDL Cholesterol Level = 133.19 + 0.4151*BMI. Which of the following is FALSE? It is estimated that for every additional unit of BMI, the Level of LDL Cholesterol increases by about 0.4. It is estimated that for the person with zero BMI, the LDL Cholesterol Level is about 133. If one person has BMI of 20, then it is estimated that the LDL Cholesterol Level is about 141. If the LDL Cholesterol Level of one person is 145, then this equation indicates that this person’s BMI is about 30. One person’s LDL Cholesterol Level is 143 and BMI is 25. Its residual is about 5.
A regression was run to determine if there is a relationship between the happiness inde expectancy in years of a given country (x). The results of the regression were: ŷ=a+bx a=-1.042 b=0.186