Hawaiian Estate Case Study

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Let X represent the amount of time it takes a student to park in the library parking lot at the university. If we know that the distribution of parking times can be modeled using an exponential distribution with a mean of 4 minutes, find the probability that it will take a randomly selected student more than 10 minutes to park in the library lot. | 0.917915 | | 0.670320 | | 0.329680 | | 0.082085 |

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Question
2 of 25
On the average, 1.8 customers per minute arrive at any one of the checkout counters of a grocery store. What type of probability distribution can be used to find out the probability that there will be no customer arriving at a checkout counter? | binomial distribution | |
Poisson distribution | | hypergeometric distribution | | None of the above. |

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Question
3 of 25
The chancellor of a major university was concerned about alcohol abuse on her campus and wanted to find out the proportion of students at her university who visited campus bars on the weekend before the final exam week. Her assistant took a random sample of 250 students and computed the portion of students in the sample who visited campus bars on the weekend before the final exam. The portion of all students at her university who visited campus bars on the weekend before the final exam week is an example of | a categorical random variable. | | a discrete random variable. | | a continuous random variable. | | a parameter. |

0 out of 6
The correct answer is: a parameter.

Question
4 of 25
TABLE 13-6
The following EXCEL tables are obtained when "Score received on an exam (measured in percentage points)" (Y) is regressed on "percentage attendance" (X) for 22 students in a Statistics for Business and Economics course.
Referring to Table 13-6, which of the following statements is true? | -2.86% of the total variability in score received can be explained by percentage attendance. | | -2.86% of the total variability in percentage attendance can be explained by score received. | | 2% of the total variability in score received can be explained by percentage attendance. | | 2% of the total variability in percentage attendance can be explained by score received. |

0 out of 6
The correct answer is:
2% of the total variability in score received can be explained by percentage attendance.

Question
5 of 25
The employees of a company were surveyed on questions regarding their educational background and marital status. Of the 600 employees, 400 had college degrees, 100 were single, and 60 were single college graduates. The probability that an employee of the company does not have a college degree is: | 0.10. | | 0.33. | | 0.67. | | 0.75. |

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Question
6 of 25
A catalog company that receives the majority of its orders by telephone conducted a study to determine how long customers were willing to wait on hold before ordering a product. The length of time was found to be a random variable best approximated by an exponential distribution with a mean equal to 3 minutes. What proportion of customers having to hold more than 4.5 minutes will hang up before placing an order? | 0.22313 | | 0.48658 | | 0.51342 | | 0.77687 |

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Question
7 of 25
Tim was planning for a meeting with his boss to discuss a raise in his annual salary. In preparation, he wanted to use the Consumer Price Index to determine the percentage increase in his real (inflation-adjusted) salary over the last three years. Which of the 4 methods of data collection was involved when he used the Consumer Price Index? | published sources | | experimentation | | surveying | | observation |

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Question
8 of 25
TABLE 13-6
The following EXCEL tables are obtained when "Score received on an exam (measured in percentage points)" (Y) is regressed on "percentage attendance" (X) for 22 students in a Statistics for Business and Economics course.
Referring to Table 13-6, which of the following statements is true? | If attendance increases by 0.341%, the estimated average score received will increase by 1 percentage point. | | If attendance increases by 1%, the estimated average score received will increase by 39.39 percentage points. | | If attendance increases by 1%, the estimated average score received will increase by 0.341 percentage points. | | If the score received increases by 39.39%, the estimated average attendance will go up by 1%. |

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Question
9 of 25
TABLE 4-2
An alcohol awareness task force at a Big-Ten university sampled 200 students after the midterm to ask them whether they went bar hopping the weekend before the midterm or spent the weekend studying, and whether they did well or poorly on the midterm. The following result was obtained. | Did Well on Midterm | Did Poorly on Midterm | Studying for Exam | 80 | 20 | Went Bar Hopping | 30 | 70 |
Referring to Table 4-2, what is the probability that a randomly selected student did well on the midterm or went bar hopping the weekend before the midterm? | 30/200 or 15% | | (80+30)/200 or 55% | | (30+70)/200 or 50% | | (80+30+70)/200 or 90% |

0 out of 6
The correct answer is:
(80+30+70)/200 or 90%

Question
10 of 25
Which of the following is a continuous quantitative variable? | the color of a student's eyes | | the number of employees of an insurance company | | the amount of milk produced by a cow in one 24-hour period | | the number of gallons of milk sold at the local grocery store yesterday |

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Question
11 of 25
TABLE 14-3
An economist is interested to see how consumption for an economy (in $ billions) is influenced by gross domestic product ($ billions) and aggregate price (consumer price index). The Microsoft Excel output of this regression is partially reproduced below.
SUMMARY OUTPUT Regression Statistics | Multiple R | 0.991 | R Square | 0.982 | Adjusted R Square | 0.976 | Standard Error | 0.299 | Observations | 10 |
ANOVA
| df | SS | MS | F | Signif F | Regression | 2 | 33.4163 | 16.7082 | 186.325 | 0.0001 | Residual | 7 | 0.6277 | 0.0897 | | | Total | 9 | 34.0440 | | | | | Coeff | StdError | t Stat | P-value | Intercept | - 0.0861 | 0.5674 | - 0.152 | 0.8837 | GDP | 0.7654 | 0.0574 | 13.340 | 0.0001 | Price | - 0.0006 | 0.0028 | - 0.219 | 0.8330 |
Referring to Table 14-3, to test for the significance of the coefficient on aggregate price index, the value of the relevant t-statistic is | 2.365 | | 0.143 | | 0.219 | | 1.960 |

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Question
12 of 25
If the outcomes of a random variable follow a Poisson distribution, then their | mean equals the standard deviation. | | median equals the standard deviation. | | mean equals the variance. | | median equals the variance.
|

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Question
13 of 25
TABLE 15-3
In Hawaii, condemnation proceedings are under way to enable private citizens to own the property that their homes are built on. Until recently, only estates were permitted to own land, and homeowners leased the land from the estate. In order to comply with the new law, a large Hawaiian estate wants to use regression analysis to estimate the fair market value of the land. The following model was fit to data collected for n = 20 properties, 10 of which are located near a cove.
Model 1: Y = β0 + β1X1 + β2X2 + β3X1X2 + β4X12 + β5X12X2 + ε where Y = Sale price of property in thousands of dollars
X1 = Size of property in thousands of square feet
X2 = 1 if property located near cove, 0 if not
Using the data collected for the 20 properties, the following partial output obtained from Microsoft Excel is shown:
SUMMARY OUTPUT Regression Statistics | Multiple R | 0.985 | R Square | 0.970 | Standard Error | 9.5 | Observations | 20 |
ANOVA
| df | SS | MS | F | Signif F | Regression | 5 | 28324 | 5664 | 62.2 | 0.0001 | Residual | 14 | 1279 | 91 | | | Total | 19 | 29063 | | | | | Coeff | StdError | t Stat | P-value
| Intercept | - 32.1 | 35.7 | - 0.90 | 0.3834 | Size | 12.2 | 5.9 | 2.05 | 0.0594 | Cove | - 104.3 | 53.5 | - 1.95 | 0.0715 | Size*Cove | 17.0 | 8.5 | 1.99 | 0.0661 | SizeSq | - 0.3 | 0.2 | - 1.28 | 0.2204 | SizeSq*Cove | - 0.3 | 0.3 | - 1.13 | 0.2749 |
Referring to Table 15-3, is the overall model statistically adequate at a 0.05 level of significance for predicting sale price (Y)? | No, since some of the t tests for the individual variables are not significant. | | No, since the standard deviation of the model is fairly large. | | Yes, since none of the β-estimates are equal to 0. | | Yes, since the p-value for the test is smaller than 0.05. |

0 out of 6
The correct answer is:
Yes, since the p-value for the test is smaller than 0.05.

Question
14 of 25
The Cp statistic is used | to determine if there is a problem of collinearity. | | if the variances of the error terms are all the same in a regression model. | | to choose the best model. | | to determine if there is an irregular component in a time series. |

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Question
15 of 25
Which of the following assumptions concerning the probability distribution of the random error term is stated incorrectly? | The distribution is normal. | | The mean of the distribution is 0. | | The variance of the distribution increases as X increases. | | The errors are independent. |

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Question
16 of 25
The probability that house sales will increase in the next 6 months is estimated to be 0.25. The probability that the interest rates on housing loans will go up in the same period is estimated to be 0.74. The probability that house sales or interest rates will go up during the next 6 months is estimated to be 0.89. The probability that both house sales and interest rates will increase during the next 6 months is: | 0.10. | | 0.185. | | 0.705. | | 0.90. |

0 out of 6
The correct answer is:
0.10.

Question
17 of 25
If n = 10 and p = 0.70, then the mean of the binomial distribution is | 0.07. | | 1.45. | | 7.00. | | 14.29. |

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Question
18 of 25
If two equally likely events A and B are collectively exhaustive, what is the probability that event A occurs? | 0 | | 0.50 | | 1.00 | | Cannot be determined from the information given. |

0 out of 6
The correct answer is:
Cannot be determined from the information given.
EXPLANATION: We do not know if they are mutually exclusive.

Question
19 of 25
TABLE 14-4
A real estate builder wishes to determine how house size (House) is influenced by family income (Income), family size (Size), and education of the head of household (School). House size is measured in hundreds of square feet, income is measured in thousands of dollars, and education is in years. The builder randomly selected 50 families and ran the multiple regression. Microsoft Excel output is provided below:
SUMMARY OUTPUT Regression Statistics | Multiple R | 0.865 | R Square | 0.748 | Adjusted R Square | 0.726 | Standard Error | 5.195 | Observations | 50 |
ANOVA
| df | SS | MS | F | Signif F | Regression | | 3605.7736 | 901.4434 | | 0.0001 | Residual | | 1214.2264 | 26.9828 | | | Total | 49 | 4820.0000 | | | | | Coeff | StdError | t Stat | P-value | Intercept | - 1.6335 | 5.8078 | - 0.281 | 0.7798 | Income | 0.4485 | 0.1137 | 3.9545 | 0.0003 | Size | 4.2615 | 0.8062 | 5.286 | 0.0001 | School | - 0.6517 | 0.4319 | - 1.509 | 0.1383 |
Referring to Table 14-4, which of the independent variables in the model are significant at the 2% level? | Income, Size, School | | Income, Size | | Size, School | | Income, School |

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Question
20 of 25
Whenever p = 0.1 and n is small, the binomial distribution will be | symmetric. | | right-skewed. | | left-skewed. | | None of the above. |

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Question
21 of 25
The personnel director at a large company studied the eating habits of the companys employees. The director noted whether employees brought their own lunches to work, ate at the company cafeteria, or went out to lunch. The goal of the study was to improve the food service at the company cafeteria. This type of data collection would best be considered as | an observational study. | | a designed experiment. | | a random sample. | | a quota sample. |

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Question
22 of 25
TABLE 14-4
A real estate builder wishes to determine how house size (House) is influenced by family income (Income), family size (Size), and education of the head of household (School). House size is measured in hundreds of square feet, income is measured in thousands of dollars, and education is in years. The builder randomly selected 50 families and ran the multiple regression. Microsoft Excel output is provided below:
SUMMARY OUTPUT Regression Statistics | Multiple R | 0.865 | R Square | 0.748 | Adjusted R Square | 0.726 | Standard Error | 5.195 | Observations | 50 |
ANOVA
| df | SS | MS | F | Signif F | Regression | | 3605.7736 | 901.4434 | | 0.0001 | Residual | | 1214.2264 | 26.9828 | | | Total | 49 | 4820.0000 | | | | | Coeff | StdError | t Stat | P-value | Intercept | - 1.6335 | 5.8078 | - 0.281 | 0.7798 | Income | 0.4485 | 0.1137 | 3.9545 | 0.0003 | Size | 4.2615 | 0.8062 | 5.286 | 0.0001 | School | - 0.6517 | 0.4319 | - 1.509 | 0.1383 |
Referring to Table 14-4, what fraction of the variability in house size is explained by income, size of family, and education? | 27.0% | | 33.4% | | 74.8% | | 86.5% |

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Question
23 of 25
The Variance Inflationary Factor (VIF) measures the | correlation of the X variables with the Y variable. | | correlation of the X variables with each other. | | contribution of each X variable with the Y variable after all other X variables are included in the model. | | standard deviation of the slope. |

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Question
24 of 25
For some value of Z, the probability that a standard normal variable is below Z is 0.2090. The value of Z is | - 0.81. | | - 0.31. | | 0.31. | | 1.96. |

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Question
25 of 25
A catalog company that receives the majority of its orders by telephone conducted a study to determine how long customers were willing to wait on hold before ordering a product. The length of time was found to be a random variable best approximated by an exponential distribution with a mean equal to 2.8 minutes. What proportion of callers is put on hold longer than 2.8 minutes? | 0.367879 | | 0.50 | | 0.60810 | | 0.632121 |

6 out of 6