**1.** TABLE 11-3
A realtor wants to compare the average sales-to-appraisal ratios of residential properties sold in four neighborhoods (A, B, C, and D). Four properties are randomly selected from each neighborhood and the ratios recorded for each, as shown below.
A: 1.2, 1.1, 0.9, 0.4 C: 1.0, 1.5, 1.1, 1.3
B: 2.5, 2.1, 1.9, 1.6 D: 0.8, 1.3, 1.1, 0.7
Interpret the results of the analysis summarized in the following table:
MA
Referring to Table 11-3, the among-group degrees of freedom is
[removed]A) 3.
[removed]B) 4.
[removed]C) 16.
[removed]D) 12.
**2.** TABLE 11-3
A realtor wants to compare the average sales-to-appraisal ratios of residential properties sold in four neighborhoods (A, B, C, and D). Four properties are randomly selected from each neighborhood and the ratios recorded for each, as shown below.
A: 1.2, 1.1, 0.9, 0.4 C: 1.0, 1.5, 1.1, 1.3
B: 2.5, 2.1, 1.9, 1.6 D: 0.8, 1.3, 1.1, 0.7
Interpret the results of the analysis summarized in the following table:
Referring to Table 11-3, the within-group sum of squares is
[removed]A) 1.0606.
[removed]B) 4.3644.
[removed]C) 1.1825.
[removed]D) 3.1819.
**3.** A completely randomized design
[removed]A) has one factor and one block and multiple values.
[removed]B) can have more than one factor, each with several treatment groups.
[removed]C) has one factor and one block.
[removed]D) has only one factor with several treatment groups.
**4.** The *F* test statistic in a one-way ANOVA is
[removed]A) MSA/MSW.
[removed]B) SSA/SSW.
[removed]C) MSW/MSA.
[removed]D) SSW/SSA.
**5.** TABLE 11-2
An airline wants to select a computer software package for its reservation system. Four software packages (1, 2, 3, and 4) are commercially available. The airline will choose the package that bumps as few passengers, on the average, as possible during a month. An experiment is set up in which each package is used to make reservations for 5 randomly selected weeks. (A total of 20 weeks was included in the experiment.) The number of passengers bumped each week is obtained, which gives rise to the following Excel output:
ANOVA
Referring to Table 11-2, the within groups degrees of freedom is
[removed]A) 19.
[removed]B) 16.
[removed]C) 4.
[removed]D) 3.
**6.** TABLE 11-5
A physician and president of a Tampa Health Maintenance Organization (HMO) are attempting to show the benefits of managed health care to an insurance company. The physician believes that certain types of doctors are more cost-effective than others. One theory is that Primary Specialty is an important factor in measuring the cost-effectiveness of physicians. To investigate this, the president obtained independent random samples of 20 HMO physicians from each of 4 primary specialties – General Practice (GP), Internal Medicine (IM), Pediatrics (PED), and Family Physicians (FP) – and recorded the total charges per member per month for each. A second factor which the president believes influences total charges per member per month is whether the doctor is a foreign or USA medical school graduate. The president theorizes that foreign graduates will have higher mean charges than USA graduates. To investigate this, the president also collected data on 20 foreign medical school graduates in each of the 4 primary specialty types described above. So information on charges for 40 doctors (20 foreign and 20 USA medical school graduates) was obtained for each of the 4 specialties. The results for the ANOVA are summarized in the following table.
Referring to Table 11-5, what assumption(s) need(s) to be made in order to conduct the test for differences between the mean charges of foreign and USA medical school graduates?
[removed]A) The charges in each group of doctors sampled are drawn from normally distributed populations.
[removed]B) The charges in each group of doctors sampled are drawn from populations with equal variances.
[removed]C) There is no significant interaction effect between the area of primary specialty and the medical school on the doctors’ mean charges.
[removed]D) All of the above are necessary assumptions.
**7.** TABLE 12-2
Many companies use well-known celebrities as spokespersons in their TV advertisements. A study was conducted to determine whether brand awareness of female TV viewers and the gender of the spokesperson are independent. Each in a sample of 300 female TV viewers was asked to identify a product advertised by a celebrity spokesperson. The gender of the spokesperson and whether or not the viewer could identify the product was recorded. The numbers in each category are given below.
Referring to Table 12-2, at 5% level of significance, the critical value of the test statistic is:
[removed]A) 3.8415
[removed]B) 13.2767
[removed]C) 5.9914
[removed]D) 9.4877
**8.** TABLE 12-18
An agronomist wants to compare the crop yield of 3 varieties of chickpea seeds. She plants all 3 varieties of the seeds on each of 5 different patches of fields. She then measures the crop yield in bushels per acre. Treating this as a randomized block design, the results are presented in the table that follows.
Referring to Table 12-18, the null hypothesis for the Friedman rank test is
[removed]A) *H*_{0}: *M*_{Field1} = *M*_{Field2} = *M*_{Field3} = *M*_{Field4} = *M*_{Field5}
[removed]B) *H*_{0}: *M*_{Smith} = *M*_{Walsh} = *M*_{Trevor}
[removed]C) *H*_{0}: *μ*_{Field1} = *μ*_{Field2} = *μ*_{Field3} = *μ*_{Field4} = *μ*_{Field5}
[removed]D) *H*_{0}: *μ*_{Smith} = *μ*_{Walsh} = *μ*_{Trevor}
**9.** TABLE 12-10
Parents complain that children read too few storybooks and watch too much television nowadays. A survey of 1,000 children reveals the following information on average time spent watching TV and average time spent reading storybooks.
Average time spent reading story books
Average time spent watching TV |
Less than
1 hour |
Between
1 and 2 hours |
More than
2 hours |
Less than 2 hours |
90 |
85 |
130 |
More than 2 hours |
655 |
32 |
8 |
Referring to Table 12-10, we want to test whether there is any relationship between average time spent watching TV and average time spent reading storybooks. Suppose the value of the test statistic was 164 (which is not the correct answer) and the critical value was 19.00 (which is not the correct answer), then we could conclude that
[removed]A) there is no connection between time spent reading storybooks and time spent watching TV.
[removed]B) there is connection between time spent reading storybooks and time spent watching TV.
[removed]C) more time spent watching TV leads to less time spent reading storybooks.
[removed]D) more time spent reading storybooks leads to less time spent watching TV.
**10.** TABLE 12-3
A computer used by a 24-hour banking service is supposed to randomly assign each transaction to one of 5 memory locations. A check at the end of a day’s transactions gave the counts shown in the table for each of the 5 memory locations, along with the number of reported errors.
The bank manager wanted to test whether the proportion of errors in transactions assigned to each of the 5 memory locations differ.
Referring to Table 12-3, the critical value of the test statistic at 1% level of significance is:
[removed]A) 13.2767
[removed]B) 7.7794
[removed]C) 23.2093
[removed]D) 20.0902
**11.** When testing for independence in a contingency table with 3 rows and 4 columns, there are ________ degrees of freedom.
[removed]A) 12
[removed]B) 5
[removed]C) 7
[removed]D) 6
**12.** TABLE 13-1
A large national bank charges local companies for using their services. A bank official reported the results of a regression analysis designed to predict the bank’s charges (*Y*)—measured in dollars per month—for services rendered to local companies. One independent variable used to predict the service charge to a company is the company’s sales revenue (*X*)—measured in millions of dollars. Data for 21 companies who use the bank’s services were used to fit the model:
*E*(*Y*) = *β*_{0} + *β*_{1}*X*
The results of the simple linear regression are provided below.
= -2,700 + 20*X*, S_{YX} = 65, two-tailed *p* value = 0.034 (for testing *β*_{1})
Referring to Table 13-1, interpret the estimate of σ, the standard deviation of the random error term (standard error of the estimate) in the model.
[removed]A) For every $1 million increase in sales revenue, we expect a service charge to increase $65.
[removed]B) About 95% of the observed service charges equal their corresponding predicted values.
[removed]C) About 95% of the observed service charges fall within $130 of the least squares line.
[removed]D) About 95% of the observed service charges fall within $65 of the least squares line.
**13.** In a simple linear regression problem, *r* and *b*_{1}
[removed]A) must have opposite signs.
[removed]B) may have opposite signs.
[removed]C) must have the same sign.
[removed]D) are equal.
**14.** The sample correlation coefficient between *X* and *Y* is 0.375. It has been found that the *p*-value is 0.256 when testing *H*_{0} : *ρ* = 0 against the two-sided alternative *H*_{1} : *ρ* ≠ 0. To test *H*_{0} : *ρ* = 0 against the one-sided alternative *H*_{1} : *ρ* < 0 at a significance level of 0.2, the *p*-value is:
[removed]A) 0.256/2
[removed]B) 1 – 0.256/2
[removed]C) (0.256)2
[removed]D) 1 – 0.256
**15.** Assuming a linear relationship between *X* and *Y*, if the coefficient of correlation (*r*) equals – 0.30,
[removed]A) the slope (*b*_{1}) is negative.
[removed]B) variable *X* is larger than variable *Y*.
[removed]C) there is no correlation.
[removed]D) the variance of *X* is negative.
**16.** In a multiple regression model, the adjusted *r*^{2}
[removed]A) cannot be negative.
[removed]B) has to fall between 0 and +1.
[removed]C) can sometimes be greater than +1.
[removed]D) can sometimes be negative.
**17.** TABLE 14-5
A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies. She proceeds to randomly select 26 large corporations and record information in millions of dollars. The Microsoft Excel output below shows results of this multiple regression.
SUMMARY OUTPUT
ANOVA
Referring to Table 14-5, what fraction of the variability in sales is explained by spending on capital and wages?
[removed]A) 68.9%
[removed]B) 50.9%
[removed]C) 83.0%
[removed]D) 27.0%
**18.** TABLE 14-1
A manager of a product sales group believes the number of sales made by an employee (*Y*) depends on how many years that employee has been with the company (*X*_{1}) and how he/she scored on a business aptitude test (*X*_{2}). A random sample of 8 employees provides the following:
Referring to Table 14-1, if an employee who had been with the company 5 years scored a 9 on the aptitude test, what would his estimated expected sales be?
[removed]A) 60.88
[removed]B) 79.09
[removed]C) 17.98
[removed]D) 55.62
**19.** To explain personal consumption (CONS) measured in dollars, data is collected for
A regression analysis was performed with CONS as the dependent variable and ln(CRDTLIM), ln(APR), ln(ADVT), and SEX as the independent variables. The estimated model was
= 2.28 – 0.29 ln(CRDTLIM) + 5.77 ln(APR) + 2.35 ln(ADVT) + 0.39 SEX
What is the correct interpretation for the estimated coefficient for SEX?
[removed]A) Holding everything else fixed, personal consumption for females is estimated to be 0.39% higher than males on the average.
[removed]B) Holding everything else fixed, personal consumption for males is estimated to be 0.39% higher than females on the average.
[removed]C) Holding everything else fixed, personal consumption for females is estimated to be $0.39 higher than males on the average.
[removed]D) Holding everything else fixed, personal consumption for males is estimated to be $0.39 higher than females on the average.
**20.** The logarithm transformation can be used
[removed]A) to overcome violations of the homoscedasticity assumption.
[removed]B) to test for possible violations of the homoscedasticity assumption.
[removed]C) to overcome violations of the autocorrelation assumption.
[removed]D) to test for possible violations of the autocorrelation assumption.
**21.** Which of the following is NOT used to determine observations that have an influential effect on the fitted model?
[removed]A) the hat matrix elements *h*_{i}
[removed]B) the *C*_{p} statistic
[removed]C) the Studentized deleted residuals *t*_{i}
[removed]D) Cook’s distance statistic
**22.** TABLE 15-2
A certain type of rare gem serves as a status symbol for many of its owners. In theory, for low prices, the demand decreases as the price of the gem increases. However, experts hypothesize that when the gem is valued at very high prices, the demand increases with price due to the status owners believe they will gain by obtaining the gem. Thus, the model proposed to best explain the demand for the gem by its price is the quadratic model:
Y = *β*_{0} + *β*_{1}*X* + *β*_{2}*X*^{2} + ε
where Y = demand (in thousands) and X = retail price per carat.
This model was fit to data collected for a sample of 12 rare gems of this type. A portion of the computer analysis obtained from Microsoft Excel is shown below:
SUMMARY OUTPUT
ANOVA
Referring to Table 15-2, what is the value of the test statistic for testing whether there is an upward curvature in the response curve relating the demand (*Y*) and the price (*X*)?
[removed]A) -5.14
[removed]B) 373
[removed]C) 0.95
[removed]D) none of the above
**23.** After estimating a trend model for annual time-series data, you obtain the following residual plot against time:
The problem with your model is that
[removed]A) the irregular component has not been accounted for.
[removed]B) the trend component has not been accounted for.
[removed]C) the cyclical component has not been accounted for.
[removed]D) the seasonal component has not been accounted for,
**24.**
TABLE 16-4
Given below are Excel outputs for various estimated autoregressive models for Coca-Cola’s real operating revenues (in billions of dollars) from 1975 to 1998. From the data, we also know that the real operating revenues for 1996, 1997, and 1998 are 11.7909, 11.7757 and 11.5537, respectively.
AR(1) Model:
AR(2)Model:
AR(3) Model:
Referring to Table 16-4, if one decides to use AR(3), what will the predicted real operating revenue for Coca-Cola be in 2001?
[removed]A) $11.68 billion
[removed]B) $11.59 billion
[removed]C) $12.47 billion
[removed]D) $11.84 billion
**25.**TABLE 16-3
The following table contains the number of complaints received in a department store for the first 6 months of last year.
Referring to Table 16-3, suppose the last two smoothed values are 81 and 96. (Note: they are not.) What would you forecast as the value of the time series for September?
[removed]A) 96
[removed]B) 81
[removed]C) 91
[removed]D) 86
**26.** A tabular presentation that shows the outcome for each decision alternative under the various states of nature is called:
[removed]A) a payback period matrix.
[removed]B) a decision tree.
[removed]C) a payoff table.
[removed]D) a decision matrix.
**27.** Blossom’s Flowers purchases roses for sale for Valentine’s Day. The roses are purchased for $10 a dozen and are sold for $20 a dozen. Any roses not sold on Valentine’s Day can be sold for $5 per dozen. The owner will purchase 1 of 3 amounts of roses for Valentine’s Day: 100, 200, or 400 dozen roses. If the probability of selling 100 dozen roses is 0.2 and 200 dozen roses is 0.5, then the probability of selling 400 dozen roses is
[removed]A) 0.7
[removed]B) 0.5
[removed]C) 0.2
[removed]D) 0.3
**28.** TABLE 17-2
The following payoff matrix is given in dollars.
Suppose the probability of Event 1 is 0.5 and Event 2 is 0.5.
Referring to Table 17-2, the *EVPI* is
[removed]A) 600
[removed]B) 400
[removed]C) 0
[removed]D) 300
**29.** TABLE 18-1
A local newspaper has 10 delivery boys who each deliver the morning paper to 50 customers every day. The owner decides to record the percentage of papers delivered on time for a 10-day period and construct a *p* chart to see whether the percentage is too erratic.
Referring to Table 18-1, which expression best characterizes the *p* chart?
[removed]A) increasing trend
[removed]B) cycles
[removed]C) in-control
[removed]D) individual outliers
**30.** One of the morals of the red bead experiment is
[removed]A) only management can change the system.
[removed]B) variation is part of the process.
[removed]C) it is the system that primarily determines performance.
[removed]D) all of the above
**31.** TABLE 11-5
A physician and president of a Tampa Health Maintenance Organization (HMO) are attempting to show the benefits of managed health care to an insurance company. The physician believes that certain types of doctors are more cost-effective than others. One theory is that Primary Specialty is an important factor in measuring the cost-effectiveness of physicians. To investigate this, the president obtained independent random samples of 20 HMO physicians from each of 4 primary specialties – General Practice (GP), Internal Medicine (IM), Pediatrics (PED), and Family Physicians (FP) – and recorded the total charges per member per month for each. A second factor which the president believes influences total charges per member per month is whether the doctor is a foreign or USA medical school graduate. The president theorizes that foreign graduates will have higher mean charges than USA graduates. To investigate this, the president also collected data on 20 foreign medical school graduates in each of the 4 primary specialty types described above. So information on charges for 40 doctors (20 foreign and 20 USA medical school graduates) was obtained for each of the 4 specialties. The results for the ANOVA are summarized in the following table.
Referring to Table 11-5, what degrees of freedom should be used to determine the critical value of the *F* ratio against which to test for differences in the mean charges for doctors among the four primary specialty areas?
[removed]A) numerator *df* = 1, denominator *df* = 159
[removed]B) numerator *df* = 3, denominator *df* = 152
[removed]C) numerator *df* = 3, denominator *df* = 159
[removed]D) numerator *df* = 1, denominator *df* = 152
**32.** Which of the following components in an ANOVA table are not additive?
[removed]A) degrees of freedom
[removed]B) sum of squares
[removed]C) mean squares
[removed]D) It is not possible to tell.
**33.**
TABLE 12-3
A computer used by a 24-hour banking service is supposed to randomly assign each transaction to one of 5 memory locations. A check at the end of a day’s transactions gave the counts shown in the table for each of the 5 memory locations, along with the number of reported errors.
The bank manager wanted to test whether the proportion of errors in transactions assigned to each of the 5 memory locations differ.
Referring to Table 12-3, which test would be used to properly analyze the data in this experiment?
[removed]A) *χ*^{2} test for differences between two proportions (related samples)
[removed]B) *χ*^{2} test for the differences among more than two proportions
[removed]C) *χ*^{2} test for differences between two proportions (independent samples)
[removed]D) *χ*^{2} test of independence
**34.** A local real estate appraiser analyzed the sales prices of homes in 2 neighborhoods to the corresponding appraised values of the homes. The goal of the analysis was to compare the distribution of sale-to-appraised ratios from homes in the 2 neighborhoods. Random and independent samples were selected from the 2 neighborhoods from last year’s homes sales, 8 from each of the 2 neighborhoods. Identify the nonparametric method that would be used to analyze the data.
[removed]A) the Wilcoxon Signed-Ranks Test, using the test statistic *W*
[removed]B) the Wilcoxon Rank Sum Test, using the test statistic *T*_{1}
[removed]C) the Wilcoxon Rank Sum Test, using the test statistic *Z*
[removed]D) the Wilcoxon Signed-Ranks Test, using the test statistic *Z*
**35.** TABLE 13-2
A candy bar manufacturer is interested in trying to estimate how sales are influenced by the price of their product. To do this, the company randomly chooses 6 small cities and offers the candy bar at different prices. Using candy bar sales as the dependent variable, the company will conduct a simple linear regression on the data below:
Referring to Table 13-2, what is the standard error of the estimate, *S*_{YX}, for the data?
[removed]A) 0.784
[removed]B) 16.299
[removed]C) 12.650
[removed]D) 0.885
**36.** TABLE 13-11
A company that has the distribution rights to home video sales of previously released movies would like to use the box office gross (in millions of dollars) to estimate the number of units (in thousands of units) that it can expect to sell. Following is the output from a simple linear regression along with the residual plot and normal probability plot obtained from a data set of 30 different movie titles:
ANOVA
Referring to Table 13-11, which of the following assumptions appears to have been violated?
[removed]A) independence of errors
[removed]B) normality of error
[removed]C) homoscedasticity
[removed]D) none of the above
**37.** TABLE 14-16
The superintendent of a school district wanted to predict the percentage of students passing a sixth-grade proficiency test. She obtained the data on percentage of students passing the proficiency test (% Passing), daily average of the percentage of students attending class (% Attendance), average teacher salary in dollars (Salaries), and instructional spending per pupil in dollars (Spending) of 47 schools in the state.
Following is the multiple regression output with *Y* = % *Passing* as the dependent variable, *X*_{1} = % *Attendance*, *X*_{2} = *Salaries* and *X*_{3} = *Spending*:
ANOVA
Referring to Table 14-16, which of the following is the correct alternative hypothesis to test whether instructional spending per pupil has any effect on percentage of students passing the proficiency test?
[removed]A) *H*_{1}: β_{3} ≠ 0
[removed]B) *H*_{1}: β_{0} ≠ 0
[removed]C) *H*_{1}: β_{2} ≠ 0
[removed]D) *H*_{1}: β_{1} ≠ 0
**38.** TABLE 15-5
The superintendent of a school district wanted to predict the percentage of students passing a sixth-grade proficiency test. She obtained the data on percentage of students passing the proficiency test (*% Passing*), daily average of the percentage of students attending class (*% Attendance*), average teacher salary in dollars (*Salaries*), and instructional spending per pupil in dollars (*Spending*) of 47 schools in the state.
Let Y = *% Passing* as the dependent variable, *X*_{1} = *% Attendance*, *X*_{2} = *Salaries* and *X*_{3} = *Spending*.
The coefficient of multiple determination (*R*) of each of the 3 predictors with all the other remaining predictors are, respectively, 0.0338, 0.4669, and 0.4743.
Following is the residual plot for % *Attendance*:
Following is the output of several multiple regression models:
Model (I):
Model (II):
Model (III):
Referring to Table 15-5, the “best” model using a 5% level of significance among those chosen by the *C*_{p} statistic is
[removed]A) *X*_{1}, *X*_{2}, *X*_{3}
[removed]B) *X*_{1}, *X*_{3}
[removed]C) either of the above
[removed]D) none of the above
**39.** TABLE 16-5
A contractor developed a multiplicative time-series model to forecast the number of contracts in future quarters, using quarterly data on number of contracts during the 3-year period from 1996 to 1998. The following is the resulting regression equation:
ln = 3.37 + 0.117 *X* – 0.083 *Q*_{1} + 1.28 *Q*_{2} + 0.617 *Q*_{3
}
where is the estimated number of contracts in a quarter
*X* is the coded quarterly value with X = 0 in the first quarter of 1996.
*Q*_{1} is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise.
*Q*_{2} is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise.
*Q*_{3} is a dummy variable equal to 1 in the third quarter of a year and 0 otherwise.
Referring to Table 16-5, using the regression equation, which of the following values is the best forecast for the number of contracts in the third quarter of 1999?
[removed]A) 252
[removed]B) 277
[removed]C) 228
[removed]D) 311
**40.** Blossom’s Flowers purchases roses for sale for Valentine’s Day. The roses are purchased for $10 a dozen and are sold for $20 a dozen. Any roses not sold on Valentine’s Day can be sold for $5 per dozen. The owner will purchase 1 of 3 amounts of roses for Valentine’s Day: 100, 200, or 400 dozen roses. Given 0.2, 0.4, and 0.4 are the probabilities for the sale of 100, 200, or 400 dozen roses, respectively, then the *EMV* for buying 200 dozen roses is
[removed]A) $4,500
[removed]B) $1,700
[removed]C) $1,000
[removed]D) $2,500
**41.** Maintaining the gains that have been made with a revised process in the long term by avoiding potential problems that can occur when a process is changed involves which part of the DMAIC process?
[removed]A) Define
[removed]B) Measure
[removed]C) Analyze
[removed]D) Improve
[removed]E) Control
**42.** TABLE 11-3
A realtor wants to compare the average sales-to-appraisal ratios of residential properties sold in four neighborhoods (A, B, C, and D). Four properties are randomly selected from each neighborhood and the ratios recorded for each, as shown below.
A: 1.2, 1.1, 0.9, 0.4 C: 1.0, 1.5, 1.1, 1.3
B: 2.5, 2.1, 1.9, 1.6 D: 0.8, 1.3, 1.1, 0.7
Interpret the results of the analysis summarized in the following table:
Referring to Table 11-3, the within-group mean squares is
[removed]A) 0.29.
[removed]B) 0.10.
[removed]C) 1.18.
[removed]D) 1.06.
**43.**TABLE 12-7
The director of transportation of a large company is interested in the usage of her van pool. She considers her routes to be divided into local and non-local. She is particularly interested in learning if there is a difference in the proportion of males and females who use the local routes. She takes a sample of a day’s riders and finds the following:
She will use this information to perform a chi-square hypothesis test using a level of significance of 0.05.
Referring to Table 12-7, the director now wants to know if the proportion of users who are male and the proportion of users who use the local routes are the same. Which test should she use?
[removed]A) *χ*^{2}-test for difference in proportions
[removed]B) Wilcoxon rank sum test
[removed]C) McNemar test for difference in proportions
[removed]D) Z-test for difference in proportions
**44.** The coefficient of determination (*r*^{2}) tells us
[removed]A) the proportion of total variation that is explained.
[removed]B) whether *r* has any significance.
[removed]C) that we should not partition the total variation.
[removed]D) that the coefficient of correlation (*r*) is larger than 1.
**45.**
TABLE 14-16
The superintendent of a school district wanted to predict the percentage of students passing a sixth-grade proficiency test. She obtained the data on percentage of students passing the proficiency test (% Passing), daily average of the percentage of students attending class (% Attendance), average teacher salary in dollars (Salaries), and instructional spending per pupil in dollars (Spending) of 47 schools in the state.
Following is the multiple regression output with *Y* = % *Passing* as the dependent variable, *X*_{1} = % *Attendance*, *X*_{2} = *Salaries* and *X*_{3} = *Spending*:
ANOVA
Referring to Table 14-16, which of the following is the correct alternative hypothesis to determine whether there is a significant relationship between percentage of students passing the proficiency test and the entire set of explanatory variables?
[removed]A) *H*_{1} : At least one of β*j* ≠ 0 for *j* = 1, 2, 3
[removed]B) *H*_{1} : β_{0} = β_{1} = β_{2} = β_{3} ≠ 0
[removed]C) *H*_{1} : At least one of β*j* ≠ 0 for *j* = 0, 1, 2, 3
[removed]D) *H*_{1} : β_{1} = β_{2} = β_{3} ≠ 0
**46.** Using the hat matrix elements *h*_{i} to determine influential points in a multiple regression model with *k* independent variable and *n* observations, *X*_{i} is an influential point if
[removed]A) *h*_{i} > *n*(*k* + 1)/2
[removed]B) *h*_{i} > 2(*k* + 1)/*n*
[removed]C) *h*_{i} < *n*(*k* + 1)/2
[removed]D) *h*_{i} < 2(*k* + 1)/*n*
**47.** The method of least squares is used on time-series data for
[removed]A) obtaining the trend equation.
[removed]B) exponentially smoothing a series.
[removed]C) eliminating irregular movements.
[removed]D) deseasonalizing the data.
**48.**
TABLE 17-1
The following payoff table shows profits associated with a set of 3 alternatives under 2 possible states of nature.
where: *S*1 is state of nature 1 *A*1 is action alternative 1
*S2* is state of nature 2 *A*2 is action alternative 2
*A*3 is action alternative 3
Referring to Table 17-1, if the probability of *S*1 is 0.5, then the coefficient of variation for *A*2 is
[removed]A) 2
[removed]B) 1.5
[removed]C) 0.231
[removed]D) 0.5
**49.** Which of the following is NOT one of Deming’s 14 points?
[removed]A) Award business on the basis of price tag alone.
[removed]B) Break down barriers between staff areas..
[removed]C) Create constancy of purpose for improvement of product or service.
[removed]D) Drive out fear.
**50.**
TABLE 11-2
An airline wants to select a computer software package for its reservation system. Four software packages (1, 2, 3, and 4) are commercially available. The airline will choose the package that bumps as few passengers, on the average, as possible during a month. An experiment is set up in which each package is used to make reservations for 5 randomly selected weeks. (A total of 20 weeks was included in the experiment.) The number of passengers bumped each week is obtained, which gives rise to the following Excel output:
ANOVA
Referring to Table 11-2, at a significance level of 1%,
[removed]A) there is insufficient evidence to conclude that the average numbers of customers bumped by the 4 packages are all the same.
[removed]B) there is insufficient evidence to conclude that the average numbers of customers bumped by the 4 packages are not all the same.
[removed]C) there is sufficient evidence to conclude that the average numbers of customers bumped by the 4 packages are all the same.
[removed]D) there is sufficient evidence to conclude that the average numbers of customers bumped by the 4 packages are not all the same. |