Business Statistics: A First Course (8th Edition)
8th Edition
ISBN: 9780135177785
Author: David M. Levine, Kathryn A. Szabat, David F. Stephan
Publisher: PEARSON
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Suppose you are estimating a wage regression, where salary is the dependent
variable and age, years of education and a dummy variable for male are your
independent variables. You are interested in measuring how salary differs between
those who have at least a college education with those who have less than a college
education. If a person is considered as having a college education when she has
more than 12 years of education, how can you measure the difference in salary
between college and non-college educated individuals?
Select one:
a. Multiply coefficient for years of education in original regression by 12
O b. Re-estimate model replacing years of education with a dummy variable for
college
c. Re-estimate model replacing years of education with a dummy variable for
college and one for no college
O d. Re-estimate model interacting years of education with a dummy variable for
college
e. Calculate the difference in predicted salary between an individual with 14
years of education and…
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.
Use your sample database that you obtained from given population database with variables EFFICIENCY (Scores range between 50 to 100), SALARY (range between 10000 to 30000 TL), JOB EXPERIENCE (range between 1 to 30 years), GENDER (male=1; female=2), DEPARTMENT (department A=1, department B=2, department C=3, ) ; perform categorical regression analysis, assuming that efficiency is dependent variable. How can i do this question in spss
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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_forwardFind the equation of the regression line for the following data set. x 1 2 3 y 0 3 4arrow_forward
- when a regression is used as a method of predicting dependent variables from one or more independent variables. How are the independent variables different from each other yet related to the dependent variable?arrow_forwardA 10-year study conducted by the American Heart Association provided data on how age, blood pressure, and smoking relate to the risk of strokes (Dataset "Stroke"). Risk is interpreted as the probability (times 100) that a person will have a stroke over the next 10-year period. For the smoker variable, 1 indicates a smoker and 0 indicates a nonsmoker. a. Develop an estimated regression equation that can be used to predict the risk of stroke given the age and blood-pressure level. b. Consider adding two independent variables to the model developed in part (a), one for the interaction between age and blood-pressure level and the other for whether the person is a smoker. Develop an estimated regression equation using these four independent variables. c. At a 0.05 level of significance, test to see whether the addition of the interaction term and the smoker variable contributes significantly to the estimated regression equation developed in part (a). d. Refer to the model developed in part…arrow_forwardIf the error term in a linear regression model is normally distributed, then the distribution of the OLS estimator, conditional on explanatory variables, is also normal. True or False, why?arrow_forward
- Accountants at the Renong and Khalid Accountant Company believed that several traveling executives were submitting unusually high travel vouchers when they returned from business trips. First, they took a sample of 200 vouchers submitted from the past year. Then they developed the following multiple-regression equation relating expected travel cost (Y) to number of days on the road (X1) and distance travelled (X2) in miles: Y = 90.00 + 48.50X1 + 0.40X2 Here is additional information concerning the regression model: Sb1 = 0.038 Sb2 =0.019 R? = 0.68 Se = 1.63 F-Statistic = 32.123 Durbin-Watson (d) statistic = 0.5436 a) Which of the independent variables appear to be statistically significant (at the 0.05 significant level) in explaining the expected travel cost for accountants? Explain. b) (Mr. Ghazali returns from a 200-mile trip that took him out of town for 5 days), what is the expected amount of his claim. c) What proportion of the total variation in expected travel cost is explained…arrow_forwardCoastal State University is conducting a study regarding the possible relationship between the cumulative grade point average and the annual income of its recent graduates. A random sample of 147 Coastal State graduates from the last five years was selected, and it was found that the least-squares regression equation relating cumulative grade point average (denoted by x, on a 4-point scale) and annual income (denoted by y, in thousands of dollars) was y = 37.79+5.51x. The standard error of the slope of this least-squares regression line was approximately 2.10. Test for a significant linear relationship between grade point average and annual income for the recent graduates of Coastal State by doing a hypothesis test regarding the population slope B1. (Assume that the variable y follows a normal distribution for each value of x and that the other regression assumptions are satisfied.) Use the 0.05 level of significance, and perform a two-tailed test. Then complete the parts below. (If…arrow_forwardSuppose a study wants to predict the market price of a certain species of turtle (Y) based on the following independent variables indicated in the table. Based from the table, what is the equation of the multiple linear regression? (Round off up to two decimal places. Market Price = 0.07 - 0.40*weight + 1.51*length + 1.41*width + 0.80*age Market Price = - 0.40*weight + 1.51*length + 1.41*width + 0.80*age Market Price = 0.07 + 0.40*weight + 1.51*length + 1.41*width + 0.80*age Market Price = 0.07 - 0.40 + weight + 1.51 + length + 1.41 + width + 0.80 + agearrow_forward
- A researcher wants to investigate the influence of the average no. of nights spent per year by the tourists from Japan on the average amount spent by them. Table 3 shows the related data obtained from the Department of Statistics Malaysia website. Table 4 shows a portion of Microsoft Excel output for the regression analysis performed based on the data in Table 3. Table 3: Data on the nights spent by tourists from Japan and amount spent Year Average no. of nights spent Average amount spent (in RM billion) 2010 5.9 1.1 2011 6.1 1.1 2012 6.1 1.4 2013 6.3 1.5 2014 6.4 1.8 2015 6.1 1.6 2016 6.2 1.3 2017 6.3 1.2 2018 6.6 1.7 2019 6.9 2.3 Table 4: Regression analysis Coefficients Standard Error t Stat P-value Intercept B0 1.4555 -3.7583 0.0056 Average no. of nights spent B1 0.2312 4.7934 0.0014 a. Note that the value of B0 and B1 are missing from Table 4.…arrow_forwardShow that an interaction term of a dummy variable and a regressor changes the slope of a regression line..arrow_forwardSuppose the athletic director at a university would like to develop a regression model to predict the point differential for games played by the men's basketball team. A point differential is the difference between the final points scored by two competing teams. A positive differential is a win, and a negative differential is a loss. For a random sample of games, the point differential was calculated, along with the number of assists, rebounds, turnovers, and personal fouls. Use the data in the accompanying table attached below to complete parts a through e below. Assume a = 0.05. a) Using technology, construct a regression model using all three independent variables. y = __ + (_)x1 + (_)x2 + (_)x3 + (_)x4 b) Test the significance of each independent variable using a= 0.10. c) interpret the p-value for each independent variable. d) Construxt a 90% confidence interval for the regression coefficients for each independent variable and interpret the meaning. e) Using the results from…arrow_forward
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