Market researchers were interested in the relationship between the number of pieces in a building-brick set and the cost of the set. The researchers collected information from a survey and used it to obtain the regression equation ý = 0.08x + 1.20, where x represents the number of pieces in a set and ý is the predicted price (in dollars) of the set. What is the predicted price of a set that has 2,520 pieces? $202.80 $2,017.20 O $3,024.08 $31,485

Market researchers were interested in the relationship between the number of pieces in a building-brick set and the cost of the set. The researchers collected information from a survey and used it to obtain the regression equation ý = 0.08x + 1.20, where x represents the number of pieces in a set and ý is the predicted price (in dollars) of the set. What is the predicted price of a set that has 2,520 pieces? $202.80 $2,017.20 O $3,024.08 $31,485

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...

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The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.
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?
Q. Which of the following is a difference between the White test and the Breusch-Pagan test? a. The White test is used for detecting autocorrelation in a linear regression model, whereas the Breusch-Pagan test is used for detecting heteroscedasticity. b. The White test is used for detecting heteroscedasticity in a linear regression model, whereas the Breusch-Pagan test is used for detecting heteroscedasticity in a quadratic regression model. c. The number of regressors used in the White test is larger than the number of regressors used in the Breusch-Pagan test. d. The number of regressors used in the Breusch-Pagan test is larger than the number of regressors used in the White test.
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. Table 7: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .854a .730 .695 6.6235 a. Predictors: (Constant), Hourly Wage Table 8: ANOVA ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1918.458 1 1918.458 129.783 .000a Residual 709.567 48 14.782 Total 2628.025 49 a. Predictors: (Constant), Hourly Wage b. Dependent Variable: Number of Complaints Table 9: Coefficients Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t…
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. Table 7: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .854a .730 .695 6.6235 a. Predictors: (Constant), Hourly Wage Table 8: ANOVA ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1918.458 1 1918.458 129.783 .000a Residual 709.567 48 14.782 Total 2628.025 49 a. Predictors: (Constant), Hourly Wage b. Dependent Variable: Number of Complaints Table 9: Coefficients Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t…
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A sociologist is interested in the relation between ?, the number of job changes and ?, the annual salary (in thousands of dollars) for people living in the Nashville area. A random sample of 10 people employed in Nashville provided the following information: X 4 7 5 6 1 5 9 10 10 3 Y 33 37 34 32 32 38 43 37 40 33 A-J answered! k) Use the regression equation to predict the annual salary of a randomly selected employee who has had 8 job changes. l) A particular employee who has had 8 job changes has an annual salary of $35,000. Calculate the residual for this data employee, to the nearest cent. m) Give a short interpretation of the y-intercept of the regression line in the context of the problem.
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A sixth-grade teacher believes that there is a relationship between his students’ IQscores (y) and the numbers of hours (x) they spend watching television each week. Thefollowing table shows a random sample of 7 sixth-grade students.y 125 116 97 114 85 107 105x 5 10 30 16 41 28 21 Does the data provide sufficient evidence to indicate that the simple linear regressionmodel is appropriate to describe the relationship between x and y? Perform a model utilitytest at α = 0.05. (Give H0, Ha, rejection region, observed test statistic, P-value, decisionand conclusion.)Find the Pearson sample correlation coefficient between x and y. Then interpretthe result.
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Shown in the SPSS output for creating a regression equation to predict students' attitudes toward statistics from their attitudes toward Ariana grande and beer. Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 4.974 0.590 8.432 0.000 AttAriana 0.264 0.155 0.256 1.701 0.097 Attbeer - 0.309 0.122 - 0.381 - 2.536 0.015 Coefficients a a Dependent Variable: Attstatistics Which of these represents the correct regression equation for this information? a. Y = 0.59 +…
7) If F (2,344) = 340.2, p < .001, then what is this saying in general about the regression model? (see p. 217) Why should you be cautious in using unstandardized beta? (p. 218)