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Residuals standardized residuals d durbin watson

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Unformatted text preview: Business Statistics 59. Multicollinearity is severe if the largest variance inflation factor (VIF) is greater than _____. A. 100 B. 5 C. 10 D. 1 60. In a multiple regression model we can conclude that multicollinearity exists if the average variance inflation factor A. 100 B. 5 C. 10 D. 1 is substantially greater than _____. 61. Multicollinearity affects the ability of the ____ statistic in assessing the importance of an independent variable. A. C B. F C. t D. VIP 62. Significant _________ may exist when the overall F-statistic is significant and the individual t statistics for all independent variables is insignificant. A. autocorrelation B. independence C. Multicollinearity D. outliers 1-1460 Chapter 01 - An Introduction to Business Statistics 63. Inclusion of redundant independent variables in the regression model increases the problem of __________. A. autocorrelation B. independence C. Multicollinearity D. outliers 64. ___________ is an iterative variable selection procedure that allows an independent variable to be added to a multiple regression model in one iteration and deleted during the next iteration? A. Logistic regression B. Stepwise regression C. Quadratic regression D. Backward regression 65. Stepwise regression uses a series of _____ tests during each iteration in order to determine which independent variables should be brought into the regression model. A. C B. F C. t D. VIF 66. When using a multiple linear regression model, data transformations on the dependent variable may be used in order to correct a violation of a(n) __________ of the multiple linear regression model. A. assumption B. independent variables C. autocorrelation D. normality 1-1461 Chapter 01 - An Introduction to Business Statistics 67. The _____________ statistic is used to test for autocorrelation among the residuals. A. Cook's distance B. C C. Durbin-Watson D. R2 68. When the assumption of __________ - residuals (error terms) is violated, the DurbinWatson statistic is used to test to determine if there is significant _____________ among the residuals. A. normality, probability B. independent, probability C. independent, autocorrelation D. normality, autocorrelation 69. If the absolute value of the ______________ is greater than t.005, then there is strong evidence that the observation is an outlier with respect to its y value. A. Cook's distance B. C statistic C. F D. studentized deleted residual 70. In a regression model a(n) ______ is an observation that is well separated from the rest of the data with respect to its y value and/or its x values. A. Outlier B. Influential C. Independent D. Dependent 71. In converting the residual to a studentized (standardized) residual for a given observation, the residual for the observation is divided by the residual's __________. A. sample size B. t value C. standard error D. square 1-1462 Chapter 01 - An Introduction to Business Statistics 72. If successive values of the residuals are close together, then there is a(n) ___________ autocorrelation, and the value of the Durbin-Watson statistic is _________. A. negative, large B. positive, small C. negative, small D. positive, large 73. If a multiple regression model has a(n) _____ statistic substantially greater than k + 1, then it can be shown that this model has substantial bias and is undesirable. A. C B. F C. Cook's distance D. Durbin-Watson 74. In general, a multiple regression model is considered to be desirable if the value of the C statistic is small and the value of C is less than _____. A. k B. k - 1 C. k + 1 D. n - k 1-1463 Chapter 01 - An Introduction to Business Statistics Essay Questions 75. Below is a partial multiple regression computer output based on a quadratic regression model. Determine the number of observations in the sample, explained variation and the MSE. 76. Below is a partial multiple regression computer output based on a quadratic regression model. Calculate R2. 1-1464 Chapter 01 - An Introduction to Business Statistics 77. Below is a partial multiple regression computer output based on a quadratic regression model. Test the overall usefulness of the model at α = .05. Calculate the F statistic and make your decision. 78. Below is a partial multiple regression computer output based on a quadratic regression model. Assuming a quadratic model was used, write the least squares prediction equation. 1-1465 Chapter 01 - An Introduction to Business Statistics 79. Below is a partial multiple regression computer output based on a quadratic regression model. Test the usefulness of the variable X2 in the model at = .05. 80. Below is a partial multiple regression computer output based on a quadratic regression model to predict student enrollment at a local university. The dependent variable is the annual enrollment given in thousands of students, the independent variable X is the increase in tuition stated in thousands of dollars per year, and X2 is the square of tuition increase given in squared thousands of dollars per year. Interp...
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