Unformatted text preview: tatistics 61. A(n) ________ plot is a residual plot that is used for the purpose of checking the
normality assumption of the multiple regression model.
A. Straight line
B. Normal
C. Scatter
D. Quadratic 62. Assumptions of a regression model can be evaluated by plotting and analyzing the
_________.
A. independent variables
B. dependent variables
C. error terms
D. beta values 63. Below is a partial multiple regression ANOVA table. How many observations were in the sample?
A. 8
B. 10
C. 12
D. 11 11315 Chapter 01  An Introduction to Business Statistics
Essay Questions 64. Below is a partial multiple regression ANOVA table. What is the total sum of squares and the degrees of freedom for total sum of squares? 65. Below is a partial multiple regression ANOVA table. What is the mean square error? 11316 Chapter 01  An Introduction to Business Statistics 66. Below is a partial multiple regression ANOVA table. Calculate the explained variation. 67. Below is a partial multiple regression ANOVA table. Calculate the proportion of the variation explained by the multiple regression model. 11317 Chapter 01  An Introduction to Business Statistics 68. Below is a partial multiple regression ANOVA table. Test the overall usefulness of the model at α = .01. Calculate F and make your decision about
whether the model is useful for prediction purposes. 69. Consider the following partial computer output for a multiple regression model. What is the total sum of squares, explained variation, and mean square error? 11318 Chapter 01  An Introduction to Business Statistics 70. Consider the following partial computer output for a multiple regression model. Calculate R2. 71. Consider the following partial computer output for a multiple regression model. Test the overall usefulness of the model at α = .01. Calculate F and make your decision. 11319 Chapter 01  An Introduction to Business Statistics 72. Consider the following analysis of variance table from a multiple regression model. Test
the model for overall usefulness at
= .01 and carefully make a managerial conclusion. 73. Below is a partial multiple regression ANOVA table. What is the total sum of squares, explained variation, mean square error and the number of
observations in the sample? 74. Below is a partial multiple regression ANOVA table. Calculate R2. 11320 Chapter 01  An Introduction to Business Statistics 75. Below is a partial multiple regression ANOVA table. Test the overall usefulness of the model at α = .05. Calculate the F statistic and make your
decision. 76. Below is a partial multiple regression ANOVA table. Calculate the adjusted R2. 77. Below is a partial multiple regression computer output. Determine the number of observations in the sample, the number of independent variables in
the model and the mean squared error. 11321 Chapter 01  An Introduction to Business Statistics 78. Below is a partial multiple regression computer output. Test the overall usefulness of the model at
decision. = .01. Calculate the F statistic and make your 79. Below is a partially completed multiple regression analysis of variance (ANOVA) table. Calculate R2. 11322 Chapter 01  An Introduction to Business Statistics 80. Below is a partial multiple regression computer output. Write the least squares prediction equation. 11323 Chapter 01  An Introduction to Business Statistics 81. Below is a partial multiple regression computer output. Test the usefulness of variable x5 in the model at α = .05. Calculate the t statistic and state
your conclusions. 11324 Chapter 01  An Introduction to Business Statistics 82. Below is a partial multiple regression computer output. Determine the 95% interval for β4 and interpret its meaning. 11325 Chapter 01  An Introduction to Business Statistics 83. The manufacturer of a light fixture believes that the dollars spent on advertising, the price
of the fixture, and the number of retail stores selling the fixture in a particular month,
influence the light fixture sales. The manufacturer randomly selects 10 months and collects
the following data: The sales are in thousands of units per month, the advertising is given in hundreds of dollars
per month, and the price is the unit retail price for the particular month. Using MINITAB, the
following computer output is obtained.
The regression equation is
Sales = 31.0 + 0.820 Advertising  0.325 Price + 1.84 Stores S = 5.465 R  Sq = 96.7% R  Sq(adj) = 95.0%
Analysis of Variance Interpret the regression coefficients for the variables advertising, price and store. 11326 Chapter 01  An Introduction to Business Statistics 84. The manufacturer of a light fixture believes that the dollars spent on advertising, the price
of the fixture and the number of retail stores selling the fixture in a particular month, influence
the light fixture sales. The manufacturer randomly selects 10 months and collects the
following data: The sales are in thousands of units per month, the advertising is given in hundreds of dollars
per month, and the pri...
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 Winter '14
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