EBK BUSINESS STATISTICS
7th Edition
ISBN: 8220102743984
Author: STEPHAN
Publisher: PEARSON
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Suppose you wanted to test whether or not the payoff to an additional year of education was the same for men and women in the STEM majors. How would you set up your regression analysis in this case
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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,…
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- What does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?arrow_forwardOlympic 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?arrow_forwardTable 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?arrow_forward
- The U.S. Postal Service is attempting to reduce the number of complaints made by the public against its workers. To facilitate this task, a staff analyst for the service regresses the number of complaints lodged against an employee last year on the hourly wage of the employee for the year. The analyst ran a simple linear regression in SPSS. The results are shown below. The current minimum wage is $5.15. If an employee earns the minimum wage, how many complaints can that employee expect to receive? Is the regression coefficient statistically significant? How can you tell?arrow_forwardA regression equation is obtained for a set of data. After examining a scatter diagram, the researcher notices a data point that is potentially an influential point. How could she confirm that this data point is indeed an influential point?arrow_forwardThe U.S. Postal Service is attempting to reduce the number of complaints made by the public against its workers. To facilitate this task, a staff analyst for the service regresses the number of complaints lodged against an employee last year on the hourly wage of the employee for the year. The analyst ran a simple linear regression in SPSS. The results are shown below. What proportion of variation in the number of complaints can be explained by hourly wages? From the results shown above, write the regression equation If wages were increased by $1.00, what is the expected effect on the number of complaints received per employee?arrow_forward
- Discuss the usefulness of regression.arrow_forwardDr. 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 61 116.2arrow_forward(4) Does how wide a possum's belly is (in cm) tell you how long a possum's tail is (in cm)? That's what re- searchers in Australia wanted to know. They built a simple linear regression model treating X as the possum's belly girth and Y as the possum's tail length. Below and at the top of the next page are all the results you will need to answer the ensuing questions, including: ● The straight line of best fit: ŷ = 30.2 + .21x • The correlation between the observed y and predicted ŷ: ry,ŷ .294 • At the top of the next page are three residual plots: (a) the plot of belly girth vs. the residuals, (b) the plot of the residuals in order of collection, (c) the histogram of the residuals = These are real data (source) consisting of measurements on each of 104 mountain brushtail possums, trapped at seven sites from Southern Victoria to central Queensland, Australia.arrow_forward
- Please help with 1.2 and 1.3 only.arrow_forward13. Examine the following regression equation and answer the questions that follow: Salary = 261,128 +91,569Goals + 16,346Assists - 585,560.Defenseman (2.789) (9.641) (3.301) (5.001) R² = 0.65 Salary = NHL Player's Salary in $ Goals Number of goals scored by that player Assists = Number of assists made by that player Defenseman = Takes on a value of 1 if player is a defenseman. Otherwise, the value of this variable is zero. The numbers in brackets are t-statistics for the variables above them. (a) What salary will an offensive player make that scores no goals and has no assists? Interpret the meaning of the R² in words. (b) (c) What is the increase in salary from scoring one more goal (holding the number of assists constant)?arrow_forwardThe following equation is the result of performing a multiple regression analysis: Job performance = 10 + (5*job knowledge) + (0.7* conscientiousness), where job knowledge is measured on a scale of 0-5 and conscientiousness is measured on a scale of 0 to 100. Which of the following conclusions is correct? !! O If a person scored 5 on job knowledge and 100 on conscientiousness he or she would have the maximum predictive score possible If a person scored 0 on both job knowledge and conscientiousness, his or her predictive score is 0 ONeither job knowledge nor conscientiousness predicts performance O Conscientiousness is less important than job knowledge. Question 3! For a measuring tool to be usefulitmus bearrow_forward
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