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Unformatted text preview: he last year to use for the chisquare test of independence. A. 23.0, 46.0 B. 27.8, 41.2 C. 189.5, 189.5 D. 34.5, 34.5 AACSB: Analytic Bloom's: Application Difficulty: Medium Learning Objective: 3 Topic: Chi-Square 1-1137 Chapter 01 - An Introduction to Business Statistics 94. In a study of car accidents and drivers who use cell phones, the following sample data are collected: At a significance level of 0.05, determine the appropriate degrees of freedom and the rejection point condition for the test. A. 1, 3.84 B. 4, 9.49 C. 2, 5.99 D. 3, 7.81 (r-1) (c-1) = 1 degree of freedom AACSB: Analytic Bloom's: Application Difficulty: Medium Learning Objective: 3 Topic: Chi-Square 1-1138 Chapter 01 - An Introduction to Business Statistics 95. In a study of car accidents and drivers who use cell phones, the following sample data are collected: Calculate the chi-square statistic for this test of independence. A. 3.84 B. 4.76 C. 1.50 D. 19.04 AACSB: Analytic Bloom's: Application Difficulty: Medium Learning Objective: 3 Topic: Chi-Square 1-1139 Chapter 01 - An Introduction to Business Statistics 96. In a study of car accidents and drivers who use cell phones, the following sample data are collected: Use a significance level of 0.05 and determine if the use of a cell phone and having an auto accident are independent (null hypothesis is they are independent). A. Reject H0 B. Fail to reject H0 Ho: Using a cell phone and having an accident are independent Ha: Using a cell phone and having an accident are dependent χ 2 = 1.5023, χ2.05,1 = 3.841 Reject Ho if χ2 > χ2.05,1 failed to reject Ho AACSB: Analytic Bloom's: Application Difficulty: Medium Learning Objective: 3 Topic: Chi-Square Chapter 13 Simple Linear Regression Analysis True / False Questions 1. The dependent variable is the variable that is being described, predicted, or controlled. True False 2. The error term is the difference between an individual value of the dependent variable and the corresponding mean value of the dependent variable. True False 3. A simple linear regression model is an equation that describes the straight-line relationship between a dependent variable and an independent variable. True False 1-1140 Chapter 01 - An Introduction to Business Statistics 4. The residual is the difference between the observed value of the dependent variable and the predicted value of the dependent variable. True False 5. The experimental region is the range of the previously observed values of the dependent variable. True False 6. The simple coefficient of determination is the proportion of total variation explained by the regression line. True False 1-1141 Chapter 01 - An Introduction to Business Statistics 7. When using simple regression analysis, if there is a strong correlation between the independent and dependent variable, then we can conclude that an increase in the value of the independent variable causes an increase in the value of the dependent variable. True False 8. When there is positive autocorrelation, over time, negative error terms are followed by positive error terms and positive error terms are followed by negative error terms. True False 9. In simple regression analysis, r2 is a percentage measure and measures the proportion of the variation explained by the simple linear regression model. True False 10. In a simple linear regression model, the coefficient of determination not only indicates the strength of the relationship between independent and dependent variable, but also shows whether the relationship is positive or negative. True False 11. In simple linear regression analysis, if the error terms exhibit a positive or negative autocorrelation over time, then the assumption of constant variance is violated. True False 12. If r = -1, then we can conclude that there is a perfect relationship between X and Y. True False 13. The correlation coefficient is the ratio of explained variation to total variation. True False 1-1142 Chapter 01 - An Introduction to Business Statistics 14. In simple linear regression analysis, we assume that the variance of the independent variable (X) is equal to the variance of the dependent variable (Y). True False 15. The slope of the simple linear regression equation represents the average change in the value of the dependent variable per unit change in the independent variable (X). True False 16. The least squares simple linear regression line minimizes the sum of the vertical deviations between the line and the data points. True False 17. The notation True False refers to the average value of the dependent variable Y. 18. A significant positive correlation between X and Y implies that changes in X cause Y to change. True False 19. The standard error of the estimate (standard error) is the estimated standard deviation of the distribution of the independent variable (X) for all values of the dependent variable (Y). True False 20. The estimated simple linear regression equation minimizes the sum of the squared deviations between each value of Y and the line. True False 1-1143 Chapter 01 - An Introduction to Business Statistics Multiple...
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## This document was uploaded on 01/20/2014.

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