EBK BUSINESS STATISTICS
7th Edition
ISBN: 9780134462783
Author: STEPHAN
Publisher: PEARSON CUSTOM PUB.(CONSIGNMENT)
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This textbook solution is under construction.
Students have asked these similar questions
a. Round off in 4 decimal places. With complete solution and box the final answer.
If the R-squared for a regression model relating the outcome y to an explanatory variable x is 0.9. This implies that there is a positive linear relationship between y and x.
True or false?
STER.
1. Wine Consumption. The table below gives the U.S. adult wine consumption, in gallons per
person per year, for selected years from 1980 to 2005.
a) Create a scatterplot for the data. Graph the scatterplot
Year
Wine
below.
Consumption
2.6
b) Determine what type of model is appropriate for the
1980
data.
1985
2.3
c) Use the appropriate regression on your calculator to find a
Graph the regression equation in the same coordinate
plane below.
d) According to your model, in what year was wine
consumption at a minimum? A
e) Use your model to predict the wine consumption in
2008.
1990
2.0
1995
2.1
2000
2.5
2005
2.8
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- 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…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. 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…arrow_forwardA researcher interested in explaining the level of foreign reserves for the country of Barbados estimated the following multiple regression model using yearly data spanning the period 2001 to 2016: Hence test whether is significant. Give reasons for your answer. Perform the F Test making sure to state the null and alternative hypothesis. Given an interpretation of the term “R-sq” and comment on its value.arrow_forward
- The 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_forward2. The instructor of a mathematics class collected data to see whether there is a correlation between the number of absences (X) and the student's score on the final exam (Y). The number of absences and score on the final exam were recorded. The following regression equation was obtained: Final score = 92.5317 – 3.7611 (Absences) a) (5 points) What would be the predicted final exam score for a student that had 10 absences? b) (5 points) The student from part (a) actually scored a 60 on the final exam. What is the residual for this student? Show all work.arrow_forwardConsider the following hypothetical regression: FRIES = 22.5 + 0.08*TRAFFIC + 9.1*COUPON? + -1.1*TEMP where FRIES is the number of pounds of fries a restaurant sells in a week, TRAFFIC is the number of people who walked by the restaurant that week (foot traffic), COUPON? is a dummy variable of if the restaurant offered a coupon or not that week (1=coupon, 0=no coupon); and TEMP is the average high that week, measured in Fahrenheit. All variables are statistically significant. If the average high is expected to be 4 degrees warmer next week, how should FRIES change? 1 Increase by 18.1 pounds 2 Decrease by 4.4 pounds 3 It is impossible to tell without knowing the values of TRAFFIC and COUPON?. 4 Increase by 4.4 pounds 5 Increase by 26.9 poundsarrow_forward
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