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Applied Statistics in Business and Economics David Doane 6th Edition Test Bank

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Applied Statistics in Business and Economics David Doane 6th Edition Test Bank

Sample Questions

Chapter 2   Data Collection

1) Categorical data have values that are described by words rather than numbers.

Explanation:  Categories are nominal data but may sometimes also be ranked (e.g., sophomore, junior, senior).

Difficulty: 1 Easy

Topic:  02.01 Variables and Data

Learning Objective:  02-02 Explain the difference between numerical and categorical data.

Bloom’s:  Remember

AACSB:  Analytical Thinking

2) Numerical data can be either discrete or continuous.

Explanation:  Numerical data can be counts (e.g., cars owned) or continuous measures (e.g., height).

Difficulty: 1 Easy

Topic:  02.01 Variables and Data

Learning Objective:  02-02 Explain the difference between numerical and categorical data.

Bloom’s:  Remember

AACSB:  Analytical Thinking

3) Categorical data are also referred to as nominal or qualitative data.

Explanation:  Categories are nominal data (nonnumerical), sometimes called qualitative data.

Difficulty: 1 Easy

Topic:  02.01 Variables and Data

Learning Objective:  02-02 Explain the difference between numerical and categorical data.

Bloom’s:  Remember

AACSB:  Analytical Thinking

4) The number of checks processed at a bank in a day is an example of categorical data.

Explanation:  Integers are numerical data.

Difficulty: 1 Easy

Topic:  02.01 Variables and Data

Learning Objective:  02-02 Explain the difference between numerical and categorical data.

Bloom’s:  Apply

AACSB:  Analytical Thinking

5) The number of planes per day that land at an airport is an example of discrete data.

Explanation:  Integers are discrete numerical data.

Difficulty: 1 Easy

Topic:  02.01 Variables and Data

Learning Objective:  02-02 Explain the difference between numerical and categorical data.

Bloom’s:  Apply

AACSB:  Analytical Thinking

6) The weight of a bag of dog food is an example of discrete data.

Explanation:  Weight is measured on a continuous scale.

Difficulty: 1 Easy

Topic:  02.01 Variables and Data

Learning Objective:  02-02 Explain the difference between numerical and categorical data.

Bloom’s:  Apply

AACSB:  Analytical Thinking

7) In last year’s annual report, Thompson Distributors indicated that it had 12 regional warehouses. This is an example of ordinal level data.

Explanation:  “Number of” is ratio data because a zero exists.

Difficulty: 1 Easy

Topic:  02.02 Level of Measurement

Learning Objective:  02-04 Recognize levels of measurement in data and ways of coding data.

Bloom’s:  Apply

AACSB:  Analytical Thinking

8) Nominal data refer to data that can be ordered in a natural way.

Explanation:  Nominal (categorical) data would be called ordinal only if categories can be ranked.

Difficulty: 1 Easy

Topic:  02.02 Level of Measurement

Learning Objective:  02-04 Recognize levels of measurement in data and ways of coding data.

Bloom’s:  Remember

AACSB:  Analytical Thinking

9) This year, Oxnard University produced two football All-Americans. This is an example of continuous data.

Explanation:  The “number of” anything is discrete.

Difficulty: 1 Easy

Topic:  02.01 Variables and Data

Learning Objective:  02-02 Explain the difference between numerical and categorical data.

Bloom’s:  Apply

AACSB:  Analytical Thinking

10) The type of statistical test that we can perform is independent of the level of measurement of the variable of interest.

Explanation:  Some statistical operations are restricted unless you have ratio or interval data.

Difficulty: 1 Easy

Topic:  02.02 Level of Measurement

Learning Objective:  02-04 Recognize levels of measurement in data and ways of coding data.

Bloom’s:  Understand

AACSB:  Analytical Thinking

11) Your weight recorded at your annual physical would not be ratio data, because you cannot have zero weight.

Explanation:  Zero is only a reference point, not necessarily an observable data value.

Difficulty: 2 Medium

Topic:  02.02 Level of Measurement

Learning Objective:  02-04 Recognize levels of measurement in data and ways of coding data.

Bloom’s:  Apply

AACSB:  Analytical Thinking

12) The level of measurement for categorical data is nominal.

Explanation:  Categorical and nominal are equivalent terms.

Difficulty: 1 Easy

Topic:  02.02 Level of Measurement

Learning Objective:  02-04 Recognize levels of measurement in data and ways of coding data.

Bloom’s:  Remember

AACSB:  Analytical Thinking

13) Temperature measured in degrees Fahrenheit is an example of interval data.

Explanation:  For temperature, scale distances are meaningful (20 to 25 is the same as 50 to 55 degrees), and 0 degrees Fahrenheit does not mean the absence of heat, so it is not a ratio measurement.

Difficulty: 2 Medium

Topic:  02.02 Level of Measurement

Learning Objective:  02-04 Recognize levels of measurement in data and ways of coding data.

Bloom’s:  Apply

AACSB:  Analytical Thinking

14) The closing price of a stock is an example of ratio data.

Explanation:  True zero exists as a reference point, whether or not it is observed.

Difficulty: 2 Medium

Topic:  02.02 Level of Measurement

Learning Objective:  02-04 Recognize levels of measurement in data and ways of coding data.

Bloom’s:  Apply

AACSB:  Analytical Thinking

15) The Statistical Abstract of the United States is a huge annual compendium of data for the United States, and it is available online free of charge.

Explanation:  In 2012 the U.S. Census Bureau ceased publishing this free compendium of data, but students can buy it for \$199 from a private publisher.

Difficulty: 1 Easy

Topic:  02.05 Data Sources

Learning Objective:  02-08 Find everyday print or electronic data sources.

Bloom’s:  Remember

AACSB:  Technology

16) Ordinal data can be treated as if it were nominal data but not vice versa.

Explanation:  You can always go back to a lower level of measurement (but not vice versa).

Difficulty: 1 Easy

Topic:  02.02 Level of Measurement

Learning Objective:  02-04 Recognize levels of measurement in data and ways of coding data.

Bloom’s:  Understand

AACSB:  Analytical Thinking

17) Responses on a seven-point Likert scale are usually treated as ratio data.

Explanation:  No true zero point exists on a Likert scale.

Difficulty: 1 Easy

Topic:  02.02 Level of Measurement

Learning Objective:  02-04 Recognize levels of measurement in data and ways of coding data.

Bloom’s:  Understand

AACSB:  Analytical Thinking

18) Likert scales are especially important in opinion polls and marketing surveys.

Explanation:  Likert scales are used in all kinds of surveys.

Difficulty: 1 Easy

Topic:  02.02 Level of Measurement

Learning Objective:  02-05 Recognize a Likert scale and know how to use it.

Bloom’s:  Remember

AACSB:  Analytical Thinking

19) Ordinal data are data that can be ranked based on some natural characteristic of the items.

Explanation:  For example, the eras Jurassic, Paleozoic, and Mesozoic can be ranked in time.

Difficulty: 1 Easy

Topic:  02.02 Level of Measurement

Learning Objective:  02-04 Recognize levels of measurement in data and ways of coding data.

Bloom’s:  Remember

AACSB:  Analytical Thinking

20) Ratio data are distinguished from interval data by the presence of a zero reference point.

Explanation:  The true zero is a reference that need not be observable.

Difficulty: 2 Medium

Topic:  02.02 Level of Measurement

Learning Objective:  02-04 Recognize levels of measurement in data and ways of coding data.

Bloom’s:  Remember

AACSB:  Analytical Thinking

21) It is better to attempt a census of a large population instead of relying on a sample.

Explanation:  A census may flounder on cost and time, while samples can be quick and accurate.

Difficulty: 2 Medium

Topic:  02.03 Sampling Concepts

Learning Objective:  02-06 Use the correct terminology for samples and populations.

Bloom’s:  Understand

AACSB:  Analytical Thinking

22) Judgment sampling and convenience sampling are nonrandom sampling techniques.

Explanation:  To be random, every item must have the same chance of being chosen.

Difficulty: 1 Easy

Topic:  02.04 Sampling Methods

Learning Objective:  02-07 Explain the common sampling methods and how to implement them.

Bloom’s:  Remember

AACSB:  Analytical Thinking

23) A problem with judgment sampling is that the sample may not reflect the population.

Explanation:  While better than mere convenience, judgment may still have flaws.

Difficulty: 1 Easy

Topic:  02.04 Sampling Methods

Learning Objective:  02-07 Explain the common sampling methods and how to implement them.

Bloom’s:  Remember

AACSB:  Analytical Thinking

24) When the population is large, a sample estimate is usually preferable to a census.

Explanation:  A census may flounder on cost and time, while samples can be quick and accurate.

Difficulty: 1 Easy

Topic:  02.03 Sampling Concepts

Learning Objective:  02-06 Use the correct terminology for samples and populations.

Bloom’s:  Understand

AACSB:  Analytical Thinking

25) Sampling error is avoidable by choosing the sample scientifically.

Explanation:  Sampling error is unavoidable, though it can be reduced by careful sampling.

Difficulty: 2 Medium

Topic:  02.04 Sampling Methods

Learning Objective:  02-07 Explain the common sampling methods and how to implement them.

Bloom’s:  Remember

AACSB:  Analytical Thinking

26) A sampling frame is used to identify the target population in a statistical study.

Explanation:  Only some portion of the population may be targeted (e.g., independent voters).

Difficulty: 2 Medium

Topic:  02.03 Sampling Concepts

Learning Objective:  02-06 Use the correct terminology for samples and populations.

Bloom’s:  Remember

AACSB:  Analytical Thinking

27) By taking a systematic sample, in which we select every 50th shopper arriving at a specific store, we are approximating a random sample of shoppers.

Explanation:  There is no bias if this method is implemented correctly.

Difficulty: 2 Medium

Topic:  02.04 Sampling Methods

Learning Objective:  02-07 Explain the common sampling methods and how to implement them.

Bloom’s:  Apply

AACSB:  Analytical Thinking

28) A worker collecting data from every other shopper who leaves a store is taking a simple random sample of customer opinion.

Explanation:  Not unless the target population is customers who shopped today (cf., all customers). Also, this is a systematic (not simple) random sample.

Difficulty: 2 Medium

Topic:  02.04 Sampling Methods

Learning Objective:  02-07 Explain the common sampling methods and how to implement them.

Bloom’s:  Apply

AACSB:  Analytical Thinking

29) Creating a list of people by taking the third name listed on every 10th page of the phone book is an example of convenience sampling.

Explanation:  This resembles two-stage cluster sampling combined with systematic sampling.

Difficulty: 2 Medium

Topic:  02.04 Sampling Methods

Learning Objective:  02-07 Explain the common sampling methods and how to implement them.

Bloom’s:  Apply

AACSB:  Analytical Thinking

30) Internet surveys posted on popular websites have no bias since anyone can reply.

Explanation:  Self-selection bias exists (respondents may be atypical).

Difficulty: 2 Medium

Topic:  02.06 Surveys

Learning Objective:  02-09 Describe basic elements of survey types, survey designs, and response scales.

Bloom’s:  Understand

AACSB:  Technology

31) Analysis of month-by-month changes in stock market prices during the most recent recession would require the use of time series data.

Explanation:  Data collected and recorded over time would be a time series.

Difficulty: 2 Medium

Topic:  02.01 Variables and Data

Learning Objective:  02-03 Explain the difference between time series and cross-sectional data.

Bloom’s:  Apply

AACSB:  Analytical Thinking

32) A cluster sample is a type of stratified sample that is based on geographical location.

Explanation:  An example would be sampling voters randomly within random zip codes.

Difficulty: 1 Easy

Topic:  02.04 Sampling Methods

Learning Objective:  02-07 Explain the common sampling methods and how to implement them.

Bloom’s:  Remember

AACSB:  Analytical Thinking

33) An advantage of a systematic sample is that no list of enumerated data items is required.

Explanation:  Systematic sampling works with a list (like random sampling) but also without one.

Difficulty: 1 Easy

Topic:  02.04 Sampling Methods

Learning Objective:  02-07 Explain the common sampling methods and how to implement them.

Bloom’s:  Remember

AACSB:  Analytical Thinking

34) Telephone surveys often have a low response rate and fail to reach the desired population.

Explanation:  Phone surveys are cheaper, but suffer from these weaknesses.

Difficulty: 1 Easy

Topic:  02.06 Surveys

Learning Objective:  02-09 Describe basic elements of survey types, survey designs, and response scales.

Bloom’s:  Remember

AACSB:  Analytical Thinking

35) Mail surveys are attractive because of their high response rates.

Explanation:  Mail surveys have low response rates and invite self-selection bias.

Difficulty: 1 Easy

Topic:  02.06 Surveys

Learning Objective:  02-09 Describe basic elements of survey types, survey designs, and response scales.

Bloom’s:  Remember

AACSB:  Analytical Thinking

36) A problem with convenience sampling is that the target population is not well-defined.

Explanation:  Convenience sampling is quick but not random, and the target population is unclear.

Difficulty: 2 Medium

Topic:  02.04 Sampling Methods

Learning Objective:  02-07 Explain the common sampling methods and how to implement them.

Bloom’s:  Remember

AACSB:  Analytical Thinking

37) If you randomly sample 50 students about their favorite places to eat, the data collected would be referred to as cross-sectional data.

Explanation:  Data for individuals would be a cross section (not a time series).

Difficulty: 2 Medium

Topic:  02.01 Variables and Data

Learning Objective:  02-03 Explain the difference between time series and cross-sectional data.

Bloom’s:  Apply

AACSB:  Analytical Thinking

38) The number of FedEx shipping centers in each of 50 cities would be ordinal level data.

Explanation:  The “number of” anything is ratio data because a true zero reference point exists.

Difficulty: 2 Medium

Topic:  02.02 Level of Measurement

Learning Objective:  02-04 Recognize levels of measurement in data and ways of coding data.

Bloom’s:  Apply

AACSB:  Analytical Thinking

39) Internet surveys posted on popular websites suffer from nonresponse bias.

Explanation:  Nonresponse or self-selection bias is rampant in such surveys.

Difficulty: 2 Medium

Topic:  02.06 Surveys

Learning Objective:  02-09 Describe basic elements of survey types, survey designs, and response scales.

Bloom’s:  Apply

AACSB:  Analytical Thinking

40) Different variables are usually shown as columns of a multivariate data set.

Explanation:  It is customary to use a <i>column</i> for each variable, while each row is an <i>observation</i>.

Difficulty: 1 Easy

Topic:  02.01 Variables and Data

Learning Objective:  02-01 Use basic terminology for describing data and samples.

Bloom’s:  Remember

AACSB:  Analytical Thinking

41) Each row in a multivariate data matrix is an observation (e.g., an individual response).

Explanation:  It is customary to use a <i>column</i> for each variable, while each row is an <i>observation</i>.

Difficulty: 1 Easy

Topic:  02.01 Variables and Data

Learning Objective:  02-01 Use basic terminology for describing data and samples.

Bloom’s:  Remember

AACSB:  Analytical Thinking

42) A bivariate data set has only two observations on a variable.

Explanation:  Bivariate refers to the number of <i>variables</i>, not the number of <i>observations</i>.

Difficulty: 1 Easy

Topic:  02.01 Variables and Data

Learning Objective:  02-01 Use basic terminology for describing data and samples.

Bloom’s:  Remember

AACSB:  Analytical Thinking

43) Running times for 3,000 runners in a 5k race would be a multivariate data set.

Explanation:  Regardless of the number of <i>observations</i>, we have only one <i>variable</i> (running time).

Difficulty: 1 Easy

Topic:  02.01 Variables and Data

Learning Objective:  02-01 Use basic terminology for describing data and samples.

Bloom’s:  Remember

AACSB:  Analytical Thinking

44) Running times for 500 runners in a 5k race would be a univariate data set.

Explanation:  Regardless of the number of <i>observations</i>, we have only one <i>variable</i> (running time).

Difficulty: 1 Easy

Topic:  02.01 Variables and Data

Learning Objective:  02-01 Use basic terminology for describing data and samples.

Bloom’s:  Remember

AACSB:  Analytical Thinking

45) A list of the salaries, ages, and years of experience for 50 CEOs is a multivariate data set.

Explanation:  We would have a data matrix with 50 rows and 3 columns.

Difficulty: 1 Easy

Topic:  02.01 Variables and Data

Learning Objective:  02-01 Use basic terminology for describing data and samples.

Bloom’s:  Remember

AACSB:  Analytical Thinking

46) The daily closing price of Apple stock over the past month would be a time series.

Explanation:  Data collected over time is a time series.

Difficulty: 2 Medium

Topic:  02.01 Variables and Data

Learning Objective:  02-03 Explain the difference between time series and cross-sectional data.

Bloom’s:  Apply

AACSB:  Analytical Thinking

47) The number of words on 50 randomly chosen textbook pages would be cross-sectional data.

Explanation:  Data were not collected over time, so we have cross-sectional data.

Difficulty: 2 Medium

Topic:  02.01 Variables and Data

Learning Objective:  02-03 Explain the difference between time series and cross-sectional data.

Bloom’s:  Apply

AACSB:  Analytical Thinking

48) A Likert scale with an even number of scale points between “Strongly Agree” and “Strongly Disagree” is intended to prevent “neutral” choices.

Explanation:  An even number of scale points (e.g., 4) forces the respondent to “lean” toward one end of the scale or the other.

Difficulty: 2 Medium

Topic:  02.02 Level of Measurement

Learning Objective:  02-05 Recognize a Likert scale and know how to use it.

Bloom’s:  Apply

AACSB:  Analytical Thinking

49) Private statistical databases (e.g., CRSP) are usually free.

Explanation:  Private research databases generally require a subscription (often expensive).

Difficulty: 1 Easy

Topic:  02.05 Data Sources

Learning Objective:  02-08 Find everyday print or electronic data sources.

Bloom’s:  Remember

AACSB:  Analytical Thinking

50) An investment firm rates bonds for Aard Co Inc. as “B+,” while bonds of Deva Corp. are rated “AA.” Which level of measurement would be appropriate for such data?

1. A) Nominal
2. B) Ordinal
3. C) Interval
4. D) Ratio

Explanation:  Ranks are clear, but interval would require assumed equal scale distances (doubtful).

Difficulty: 2 Medium

Topic:  02.02 Level of Measurement

Learning Objective:  02-04 Recognize levels of measurement in data and ways of coding data.

Bloom’s:  Evaluate

AACSB:  Analytical Thinking

51) Which variable is least likely to be regarded as ratio data?

1. A) Length of time required for a randomly chosen vehicle to cross a toll bridge (minutes)
2. B) Weight of a randomly chosen student (pounds)
3. C) Number of fatalities in a randomly chosen traffic disaster (persons)
4. D) Student’s evaluation of a professor’s teaching (Likert scale)

Explanation:  A Likert scale has no true zero. The other examples do.

Difficulty: 2 Medium

Topic:  02.02 Level of Measurement

Learning Objective:  02-04 Recognize levels of measurement in data and ways of coding data.

Bloom’s:  Apply

AACSB:  Analytical Thinking

52) Which of the following is numerical data?

2. B) The brand of cell phone you own
3. C) Whether you have an American Express card
4. D) The fuel economy (MPG) of your car

Explanation:  Fuel economy is numerical. The others are categorical.

Difficulty: 1 Easy

Topic:  02.01 Variables and Data

Learning Objective:  02-02 Explain the difference between numerical and categorical data.

Bloom’s:  Apply

AACSB:  Analytical Thinking

53) Measurements from a sample are called

1. A) statistics.
2. B) inferences.
3. C) parameters.
4. D) variables.

Explanation:  A measurement calculated from a sample is a statistic.

Difficulty: 1 Easy

Topic:  02.04 Sampling Methods

Learning Objective:  02-06 Use the correct terminology for samples and populations.

Bloom’s:  Remember

AACSB:  Analytical Thinking

54) Quantitative variables use which two levels of measurement?

1. A) Ordinal and ratio
2. B) Interval and ordinal
3. C) Nominal and ordinal
4. D) Interval and ratio

Explanation:  Numerical (quantitative) data can be interval or ratio.

Difficulty: 2 Medium

Topic:  02.02 Level of Measurement

Learning Objective:  02-04 Recognize levels of measurement in data and ways of coding data.

Bloom’s:  Remember

AACSB:  Analytical Thinking

55) Temperature in degrees Fahrenheit is an example of a(n) ________ variable.

1. A) nominal
2. B) ordinal
3. C) interval
4. D) ratio

Explanation:  No true zero exists in temperature measurements except on the Kelvin scale.

Difficulty: 1 Easy

Topic:  02.02 Level of Measurement

Learning Objective:  02-04 Recognize levels of measurement in data and ways of coding data.

Bloom’s:  Apply

AACSB:  Analytical Thinking

56) Using a sample to make generalizations about an aspect of a population is called

1. A) data mining.
2. B) descriptive statistics.
3. C) random sampling.
4. D) statistical inference.

Explanation:  Generalizing from a sample to a population is an inference.

Difficulty: 1 Easy

Topic:  02.03 Sampling Concepts

Learning Objective:  02-06 Use the correct terminology for samples and populations.

Bloom’s:  Remember

AACSB:  Analytical Thinking

57) Your telephone area code is an example of a(n) ________ variable.

1. A) nominal
2. B) ordinal
3. C) interval
4. D) ratio

Explanation:  Area codes are not ranked, so they are merely nominal (i.e., categorical).

Difficulty: 2 Medium

Topic:  02.02 Level of Measurement

Learning Objective:  02-04 Recognize levels of measurement in data and ways of coding data.

Bloom’s:  Understand

AACSB:  Analytical Thinking

58) Which is least likely to be regarded as a ratio variable?

1. A) A critic’s rating of a restaurant on a 1 to 4 scale
2. B) Automobile exhaust emission of nitrogen dioxide (milligrams per mile)
3. C) Number of customer complaints per day at a cable TV company office
4. D) Cost of an eBay purchase

Explanation:  Ratings on a Likert scale have no meaningful zero.

Difficulty: 2 Medium

Topic:  02.02 Level of Measurement

Learning Objective:  02-04 Recognize levels of measurement in data and ways of coding data.

Bloom’s:  Apply

AACSB:  Analytical Thinking

59) Automobile exhaust emission of CO2 (milligrams per mile) is ________ data.

1. A) nominal
2. B) ordinal
3. C) interval
4. D) ratio

Explanation:  Meaningful zero emissions are possible (e.g., electric car) so ratio.

Difficulty: 2 Medium

Topic:  02.02 Level of Measurement

Learning Objective:  02-04 Recognize levels of measurement in data and ways of coding data.

Bloom’s:  Apply

AACSB:  Analytical Thinking

60) Your rating of the food served at a local restaurant using a three-point scale of 0 = gross, 1 = decent, 2 = yummy is ________ data.

1. A) nominal
2. B) ordinal
3. C) interval
4. D) ratio

Explanation:  Only rankings are implied (not equal scale distances).

Difficulty: 2 Medium

Topic:  02.02 Level of Measurement

Learning Objective:  02-04 Recognize levels of measurement in data and ways of coding data.

Bloom’s:  Apply

AACSB:  Analytical Thinking

61) The number of passengers “bumped” on a particular airline flight is ________ data.

1. A) nominal
2. B) ordinal
3. C) interval
4. D) ratio

Explanation:  A true zero point exists (no passengers might be bumped).

Difficulty: 1 Easy

Topic:  02.02 Level of Measurement

Learning Objective:  02-04 Recognize levels of measurement in data and ways of coding data.

Bloom’s:  Apply

AACSB:  Analytical Thinking

62) Which should not be regarded as a continuous random variable?

1. A) Tonnage carried by a randomly chosen oil tanker at sea
2. B) Wind velocity at 7 o’clock this morning
3. C) Number of personal fouls by the Miami Heat in a game
4. D) Length of time to play a Wimbledon tennis match

Explanation:  Counting things yields integer (discrete) data.

Difficulty: 2 Medium

Topic:  02.01 Variables and Data

Learning Objective:  02-02 Explain the difference between numerical and categorical data.

Bloom’s:  Apply

AACSB:  Analytical Thinking

63) Which of the following is not true?

1. A) Categorical data have values that are described by words rather than numbers.
2. B) Categorical data are also referred to as nominal or qualitative data.
3. C) The number of checks processed at a bank in a day is categorical data.
4. D) Numerical data can be either discrete or continuous.

Explanation:  The “number of” anything is a discrete numerical variable.

Difficulty: 2 Medium

Topic:  02.01 Variables and Data

Learning Objective:  02-02 Explain the difference between numerical and categorical data.

Bloom’s:  Apply

AACSB:  Analytical Thinking

64) Which of the following is true?

1. A) The type of charge card used by a customer (Visa, MasterCard, AmEx) is ordinal data.
2. B) The duration (minutes) of a flight from Boston to Minneapolis is ratio data.
3. C) The number of Nobel Prize–winning faculty at Oxnard University is continuous data.
4. D) The number of regional warehouses owned by Jankord Industries is ordinal data.

Explanation:  A true zero exists as a reference point (even if not observed), so ratios have meaning.

Difficulty: 2 Medium

Topic:  02.02 Level of Measurement

Learning Objective:  02-04 Recognize levels of measurement in data and ways of coding data.

Bloom’s:  Apply

AACSB:  Analytical Thinking

65) Which statement is correct?

1. A) Judgment sampling is preferred to systematic sampling.
2. B) Sampling without replacement introduces bias in our estimates of parameters.
3. C) Cluster sampling is useful when strata characteristics are unknown.
4. D) Focus groups usually work best without a moderator.

Explanation:  Review the characteristics of each sampling method.

Difficulty: 2 Medium

Topic:  02.04 Sampling Methods

Learning Objective:  02-07 Explain the common sampling methods and how to implement them.

Bloom’s:  Remember

AACSB:  Analytical Thinking

66) A Likert scale

1. A) yields interval data if scale distances are equal.
2. B) must have an odd number of scale points.
3. C) must have a verbal label on each scale point.
4. D) is rarely used in marketing surveys.

Explanation:  Marketers use Likert scales and try to make scales with meaningful intervals.

Difficulty: 2 Medium

Topic:  02.02 Level of Measurement

Learning Objective:  02-05 Recognize a Likert scale and know how to use it.

Bloom’s:  Remember

AACSB:  Analytical Thinking

67) Which is most nearly correct regarding sampling error?

1. A) It can be eliminated by increasing the sample size.
2. B) It cannot be eliminated by any statistical sampling method.
3. C) It can be eliminated by using Excel’s =RANDBETWEEN() function.
4. D) It can be eliminated by utilizing systematic random sampling.

Explanation:  Sampling involves error, though it can be minimized by proper methodology.

Difficulty: 2 Medium

Topic:  02.03 Sampling Concepts

Learning Objective:  02-06 Use the correct terminology for samples and populations.

Bloom’s:  Understand

AACSB:  Analytical Thinking

Applied Statistics in Business and Economics, 6e (Doane)

Chapter 4   Descriptive Statistics

1) A data set with two values that are tied for the highest number of occurrences is called bimodal.

Explanation:  “Bimodal” means two modes.

Difficulty: 1 Easy

Topic:  04.02 Measures of Center

Learning Objective:  04-01 Explain the concepts of center, variability, and shape.

Bloom’s:  Remember

AACSB:  Analytical Thinking

2) The midrange is not greatly affected by outliers.

Explanation:  Extremes distort the midrange (average of highest and lowest data values).

Difficulty: 1 Easy

Topic:  04.02 Measures of Center

Learning Objective:  04-02 Calculate and interpret common measures of center.

Bloom’s:  Remember

AACSB:  Analytical Thinking

3) The second quartile is the same as the median.

Explanation:  The second quartile, the median, and the 50th percentile are the same thing.

Difficulty: 1 Easy

Topic:  04.05 Percentiles, Quartiles, and Box Plots

Learning Objective:  04-07 Calculate quartiles and other percentiles.

Bloom’s:  Remember

AACSB:  Analytical Thinking

4) A trimmed mean may be preferable to a mean when a data set has extreme values.

Explanation:  Trimming diminishes the effect of outliers.

Difficulty: 1 Easy

Topic:  04.02 Measures of Center

Learning Objective:  04-02 Calculate and interpret common measures of center.

Bloom’s:  Understand

AACSB:  Analytical Thinking

5) One benefit of the box plot is that it clearly displays the standard deviation.

Explanation:  A box plot shows quartiles.

Difficulty: 2 Medium

Topic:  04.05 Percentiles, Quartiles, and Box Plots

Learning Objective:  04-08 Make and interpret box plots.

Bloom’s:  Understand

AACSB:  Analytical Thinking

6) It is inappropriate to apply the Empirical Rule to a population that is right-skewed.

Explanation:  The Empirical Rule applies to normal populations.

Difficulty: 2 Medium

Topic:  04.04 Standardized Data

Learning Objective:  04-05 Apply the Empirical Rule and recognize outliers.

Bloom’s:  Apply

AACSB:  Analytical Thinking

7) Given the data set 10, 5, 2, 6, 3, 4, 20, the median value is 5.

Explanation:  Sort and find the middle value.

Difficulty: 2 Medium

Topic:  04.02 Measures of Center

Learning Objective:  04-02 Calculate and interpret common measures of center.

Bloom’s:  Apply

AACSB:  Analytical Thinking

8) Given the data set 2, 5, 10, 6, 3, the median value is 3.

Explanation:  Sort and find the middle value: 2  3  5  6  10.

Difficulty: 2 Medium

Topic:  04.02 Measures of Center

Learning Objective:  04-02 Calculate and interpret common measures of center.

Bloom’s:  Apply

AACSB:  Analytical Thinking

9) When data are right-skewed, we expect the median to be greater than the mean.

Explanation:  It is the other way around, as the mean will be pulled up by extremes.

Difficulty: 1 Easy

Topic:  04.02 Measures of Center

Learning Objective:  04-01 Explain the concepts of center, variability, and shape.

Bloom’s:  Remember

AACSB:  Analytical Thinking

10) The sum of the deviations around the mean is always zero.

Explanation:  The mean is the fulcrum (balancing point), so deviations must sum to zero.

Difficulty: 2 Medium

Topic:  04.02 Measures of Center

Learning Objective:  04-02 Calculate and interpret common measures of center.

Bloom’s:  Remember

AACSB:  Analytical Thinking

11) The midhinge is a robust measure of center when there are outliers.

Explanation:  Outliers have little effect on the midhinge (average of the 25th and 75th percentiles).

Difficulty: 2 Medium

Topic:  04.05 Percentiles, Quartiles, and Box Plots

Learning Objective:  04-07 Calculate quartiles and other percentiles.

Bloom’s:  Understand

AACSB:  Analytical Thinking

12) Chebyshev’s Theorem says that at most 50 percent of the data lie within 2 standard deviations of the mean.

Explanation:  At least 75 percent by Chebyshev.

Difficulty: 2 Medium

Topic:  04.04 Standardized Data

Learning Objective:  04-04 Apply Chebyshev’s theorem.

Bloom’s:  Apply

AACSB:  Analytical Thinking

13) Chebyshev’s Theorem says that at least 95 percent of the data lie within 2 standard deviations of the mean.

Explanation:  At least 75 percent by Chebyshev.

Difficulty: 2 Medium

Topic:  04.04 Standardized Data

Learning Objective:  04-04 Apply Chebyshev’s theorem.

Bloom’s:  Apply

AACSB:  Analytical Thinking

14) If there are 19 data values, the median will have 10 values above it and 9 below it since nis odd.

Explanation:  When n is odd, the median is the middle member of the sorted data set. In this case, the median is x10 and there will be 9 below x10 (x1, . . . ,x9) and 9 above x10 (x11, . . . , x19).

Difficulty: 2 Medium

Topic:  04.02 Measures of Center

Learning Objective:  04-02 Calculate and interpret common measures of center.

Bloom’s:  Apply

AACSB:  Analytical Thinking

15) If there are 20 data values, the median will be halfway between two data values.

Explanation:  Median is between two data values when n is even.

Difficulty: 2 Medium

Topic:  04.02 Measures of Center

Learning Objective:  04-02 Calculate and interpret common measures of center.

Bloom’s:  Apply

AACSB:  Analytical Thinking

16) In a left-skewed distribution, we expect that the median will be greater than the mean.

Explanation:  The mean is likely to be pulled down by low extremes.

Difficulty: 1 Easy

Topic:  04.02 Measures of Center

Learning Objective:  04-01 Explain the concepts of center, variability, and shape.

Bloom’s:  Understand

AACSB:  Analytical Thinking

17) If the standard deviations of two samples are the same, so are their coefficients of variation.

Explanation:  The means may differ, which affects their coefficients of variation.

Difficulty: 2 Medium

Topic:  04.03 Measures of Variability

Learning Objective:  04-03 Calculate and interpret common measures of variability.

Bloom’s:  Apply

AACSB:  Analytical Thinking

18) A certain health maintenance organization (HMO) examined the number of office visits by its members in the last year. This data set would probably be skewed to the left due to low outliers.

Explanation:  The lower bound is zero, but high extremes are likely for sicker individuals.

Difficulty: 3 Hard

Topic:  04.02 Measures of Center

Learning Objective:  04-01 Explain the concepts of center, variability, and shape.

Bloom’s:  Evaluate

AACSB:  Analytical Thinking

19) A certain health maintenance organization examined the number of office visits by its members in the last year. For this data set, the mean is probably not a very good measure of a “typical” person’s office visits.

Explanation:  The lower bound is zero, but high extremes are likely for sicker individuals.

Difficulty: 3 Hard

Topic:  04.02 Measures of Center

Learning Objective:  04-02 Calculate and interpret common measures of center.

Bloom’s:  Evaluate

AACSB:  Analytical Thinking

20) Referring to this box plot of ice cream fat content, the median seems more “typical” of fat content than the midrange as a measure of center.

Explanation:  The midrange (average of low and high) will be pulled down by the left-tail minimum in this left-skewed distribution.

Difficulty: 2 Medium

Topic:  04.05 Percentiles, Quartiles, and Box Plots

Learning Objective:  04-08 Make and interpret box plots.

Bloom’s:  Apply

AACSB:  Analytical Thinking

21) Referring to this box plot of ice cream fat content, the mean would exceed the median.

Explanation:  The data are skewed left.

Difficulty: 2 Medium

Topic:  04.05 Percentiles, Quartiles, and Box Plots

Learning Objective:  04-08 Make and interpret box plots.

Bloom’s:  Apply

AACSB:  Analytical Thinking

22) Referring to this box plot of ice cream fat content, the skewness would be negative.

Explanation:  The data are skewed left (negative skewness) as indicated by the long left tail.

Difficulty: 2 Medium

Topic:  04.05 Percentiles, Quartiles, and Box Plots

Learning Objective:  04-08 Make and interpret box plots.

Bloom’s:  Apply

AACSB:  Analytical Thinking

23) Referring to this graph of ice cream fat content, the second quartile is between 60 and 61.

Explanation:  Drop a line to lower axis to hit between 60 and 61.

Difficulty: 2 Medium

Topic:  04.05 Percentiles, Quartiles, and Box Plots

Learning Objective:  04-08 Make and interpret box plots.

Bloom’s:  Apply

AACSB:  Analytical Thinking

24) The range as a measure of variability is very sensitive to extreme data values.

Explanation:  The range depends only on highest and lowest data values, so it is easily distorted.

Difficulty: 1 Easy

Topic:  04.03 Measures of Variability

Learning Objective:  04-03 Calculate and interpret common measures of variability.

Bloom’s:  Understand

AACSB:  Analytical Thinking

25) In calculating the sample variance, the sum of the squared deviations around the mean is divided by n − 1 to avoid underestimating the unknown population variance.

Explanation:  Check the definition. You lose one piece of information because the mean is estimated.

Difficulty: 2 Medium

Topic:  04.03 Measures of Variability

Learning Objective:  04-03 Calculate and interpret common measures of variability.

Bloom’s:  Understand

AACSB:  Analytical Thinking

26) Outliers are data values that fall beyond ±2 standard deviations from the mean.

Explanation:  Outliers are 3 standard deviations from the mean

Difficulty: 1 Easy

Topic:  04.04 Standardized Data

Learning Objective:  04-05 Apply the Empirical Rule and recognize outliers.

Bloom’s:  Remember

AACSB:  Analytical Thinking

27) The Empirical Rule assumes that the distribution of data follows a normal curve.

Explanation:  Unlike Chebyshev, the Empirical Rule assumes a normal population.

Difficulty: 1 Easy

Topic:  04.04 Standardized Data

Learning Objective:  04-05 Apply the Empirical Rule and recognize outliers.

Bloom’s:  Remember

AACSB:  Analytical Thinking

28) The Empirical Rule can be applied to any distribution, unlike Chebyshev’s theorem.

Explanation:  The Empirical Rule assumes a normal population, while Chebyshev applies to any population.

Difficulty: 2 Medium

Topic:  04.04 Standardized Data

Learning Objective:  04-05 Apply the Empirical Rule and recognize outliers.

Bloom’s:  Understand

AACSB:  Analytical Thinking

29) When applying the Empirical Rule to a distribution of grades, if a student scored one standard deviation below the mean, then she would be at the 25th percentile of the distribution.

Explanation:  About 15.87 percent (not 25 percent) are less than one standard deviation below the mean (in a normal distribution).

Difficulty: 2 Medium

Topic:  04.04 Standardized Data

Learning Objective:  04-05 Apply the Empirical Rule and recognize outliers.

Bloom’s:  Apply

AACSB:  Analytical Thinking

30) Kurtosis cannot be judged accurately by looking at a histogram.

Explanation:  Histograms are affected by scaling, so peakedness is hard to judge.

Difficulty: 2 Medium

Topic:  04.08 Skewness and Kurtosis

Learning Objective:  04-11 Assess skewness and kurtosis in a sample.

Bloom’s:  Apply

AACSB:  Analytical Thinking

31) A platykurtic distribution is more sharply peaked (i.e., thinner tails) than a normal distribution.

Explanation:  A platykurtic distribution is flatter than a normal distribution (thicker tails).

Difficulty: 2 Medium

Topic:  04.08 Skewness and Kurtosis

Learning Objective:  04-11 Assess skewness and kurtosis in a sample.

Bloom’s:  Remember

AACSB:  Analytical Thinking

32) A leptokurtic distribution is more sharply peaked (i.e., thinner tails) than a normal distribution.

Explanation:  A leptokurtic distribution is more sharply peaked and has thinner tails.

Difficulty: 2 Medium

Topic:  04.08 Skewness and Kurtosis

Learning Objective:  04-11 Assess skewness and kurtosis in a sample.

Bloom’s:  Remember

AACSB:  Analytical Thinking

33) A positive kurtosis coefficient in Excel indicates a leptokurtic condition in a distribution.

Explanation:  The sign of Excel’s kurtosis coefficient indicates the kurtosis direction relative to a normal distribution.

Difficulty: 2 Medium

Topic:  04.08 Skewness and Kurtosis

Learning Objective:  04-11 Assess skewness and kurtosis in a sample.

Bloom’s:  Remember

AACSB:  Analytical Thinking

34) A sample consists of the following data: 7, 11, 12, 18, 20, 22, 43. Using the “three standard deviation” criterion, the last observation (X = 43) would be considered an outlier.

Explanation:  The observation 43 is not more than three standard deviations above the mean for this data set. The sample mean is 19.00 and the sample standard deviation is 11.86.

Difficulty: 3 Hard

Topic:  04.04 Standardized Data

Learning Objective:  04-06 Transform a data set into standardized values.

Bloom’s:  Apply

AACSB:  Analytical Thinking

35) The coefficient of variation is

1. A) measured on a scale from 0 to 100.
2. B) a unit-free statistic.
3. C) helpful when the sample means are zero.
4. D) a measure of correlation for two variables.

Explanation:  The coefficient of variation is unit free. It is the standard deviation as a percentage of the mean. But it cannot be used when the mean is zero because the mean is in the denominator.

Difficulty: 1 Easy

Topic:  04.03 Measures of Variability

Learning Objective:  04-03 Calculate and interpret common measures of variability.

Bloom’s:  Remember

AACSB:  Analytical Thinking

36) Which is not an advantage of the method of medians to find Q1 and Q3?

1. A) Ease of interpolating quartile positions
2. B) Ease of application in small data sets
3. C) Intuitive definitions without complex formulas
4. D) Same method as Excel’s =QUARTILE.EXC function.

Explanation:  When the quartiles lie between two data values, the method of medians goes halfway between the values (very simple), while Excel interpolates between them in a more complex way.

Difficulty: 2 Medium

Topic:  04.05 Percentiles, Quartiles, and Box Plots

Learning Objective:  04-07 Calculate quartiles and other percentiles.

Bloom’s:  Understand

AACSB:  Analytical Thinking

37) Which is a characteristic of the mean as a measure of center?

1. A) Deviations do not sum to zero when there are extreme values.
2. B) It is less reliable than the mode when the data are continuous.
3. C) It utilizes all the information in a sample.
4. D) It is usually equal to the median in business data.

Explanation:  The mean utilizes all n data values. Deviations always sum to zero around the mean. The mean works for continuous data (unlike the mode). The mean often differs from the median in business data.

Difficulty: 2 Medium

Topic:  04.02 Measures of Center

Learning Objective:  04-02 Calculate and interpret common measures of center.

Bloom’s:  Understand

AACSB:  Analytical Thinking

38) The position of the median is

1. A) n/2 in any sample.
2. B) n/2 if n is even.
3. C) n/2 if n is odd.
4. D) (n+1)/2 in any sample.

Explanation:  This formula always works for the median position. For example, if n = 10 (even) the median is at position (10+1)/2 = 5.5, or halfway between x5 and x6. But if n = 11 (odd) the median is at position (11+1)/2 = 6, which is observation x6.

Difficulty: 3 Hard

Topic:  04.02 Measures of Center

Learning Objective:  04-02 Calculate and interpret common measures of center.

Bloom’s:  Apply

AACSB:  Analytical Thinking

39) Which is a characteristic of the trimmed mean as a measure of center?

1. A) It is similar to the mean if there are offsetting high and low extremes.
2. B) It is especially helpful in a small sample.
3. C) It does not require sorting the sample.
4. D) It is basically the same as the midrange.

Explanation:  After sorting, we can trim unusual values to stabilize the mean. The trimmed mean may be similar to the mean if the extremes on either end offset each other. Unlike the trimmed mean, the midrange is affected by outliers.

Difficulty: 2 Medium

Topic:  04.02 Measures of Center

Learning Objective:  04-02 Calculate and interpret common measures of center.

Bloom’s:  Understand

AACSB:  Analytical Thinking

40) Which is not a characteristic of the geometric mean as a measure of center?

1. A) It is similar to the mean if the data are skewed right.
2. B) It mitigates the effects of large data values.
3. C) It is useful in business data to calculate average growth rates.
4. D) It cannot be calculated when the data contain negative or zero values.

Explanation:  Although both the mean and the geometric mean are affected by high extremes in skewed data, the geometric mean tends to reduce their influence. The geometric mean cannot be used when any data values are zero or negative.

Difficulty: 2 Medium

Topic:  04.02 Measures of Center

Learning Objective:  04-02 Calculate and interpret common measures of center.

Bloom’s:  Understand

AACSB:  Analytical Thinking

41) Which is not a characteristic of the standard deviation?

1. A) It is always the square root of the variance.
2. B) It is not applicable when data are continuous.
3. C) It can be calculated when the data contain negative or zero values.
4. D) Its physical interpretation is not as easy as the MAD.

Explanation:  The standard deviation applies to any data measured on a ratio or interval scale. Because it is a square root, its visual interpretation may be less clear than the MAD.

Difficulty: 2 Medium

Topic:  04.03 Measures of Variability

Learning Objective:  04-03 Calculate and interpret common measures of variability.

Bloom’s:  Remember

AACSB:  Analytical Thinking

42) Chebyshev’s Theorem

1. A) applies to all samples.
2. B) applies only to samples from a normal population.
3. C) gives a narrower range of predictions than the Empirical Rule.
4. D) is based on Sturges’ Rule for data classification.

Explanation:  The strength of Chebyshev’s Theorem is that it makes no assumption about normality, while the Empirical Rule only works for normal populations.

Difficulty: 2 Medium

Topic:  04.04 Standardized Data

Learning Objective:  04-04 Apply Chebyshev’s theorem.

Bloom’s:  Remember

AACSB:  Analytical Thinking

43) Which of the following is not a valid description of an outlier?

1. A) A data value beyond the outer fences
2. B) A data value that is very unusual
3. C) A data value that lies below Q1or above Q3
4. D) A data value several standard deviations from the mean.

Explanation:  Data values outside the quartiles (top or bottom 25 percent) are not very unusual.

Difficulty: 2 Medium

Topic:  04.04 Standardized Data

Learning Objective:  04-05 Apply the Empirical Rule and recognize outliers.

Bloom’s:  Apply

AACSB:  Analytical Thinking

44) If samples are from a normal distribution with μ = 100 and σ = 10, we expect

1. A) about 68 percent of the data within 90 to 110.
2. B) almost all the data within 80 to 120.
3. C) about 95 percent of the data within 70 to 130.
4. D) about half the data to exceed 75.

Explanation:  Review the Empirical Rule. For example, the interval 90 to 110 is the μ ± 1σ range.

Difficulty: 2 Medium

Topic:  04.04 Standardized Data

Learning Objective:  04-05 Apply the Empirical Rule and recognize outliers.

Bloom’s:  Apply

AACSB:  Analytical Thinking

45) In a sample of 10,000 observations from a normal population, how many would you expect to lie beyond three standard deviations of the mean?

1. A) None of them

Explanation:  Within μ ± 3σ we would expect 99.73 percent of 10,000, or 9,973 data values.

Difficulty: 2 Medium

Topic:  04.04 Standardized Data

Learning Objective:  04-05 Apply the Empirical Rule and recognize outliers.

Bloom’s:  Apply

AACSB:  Analytical Thinking

46) Which is the Excel formula for the standard deviation of a sample array named Data?

1. A) =STDEV.S(Data)
2. B) =STANDEV(Data)
3. C) =STDEV.P(Data)
4. D) =SUM(Data)/(COUNT(Data)-1)

Explanation:  STDEV.S(Data) denotes a sample standard deviation.

Difficulty: 2 Medium

Topic:  04.03 Measures of Variability

Learning Objective:  04-03 Calculate and interpret common measures of variability.

Bloom’s:  Remember

AACSB:  Technology

47) Which is not true of an outlier?

1. A) It is likely to be from a different population.
2. B) It suggests an error in recording the data.
3. C) It is best discarded to get a better mean.
4. D) It is an anomaly that may tell the researcher something.

Explanation:  We are reluctant to delete outliers, as they may tell us something important.

Difficulty: 1 Easy

Topic:  04.04 Standardized Data

Learning Objective:  04-05 Apply the Empirical Rule and recognize outliers.

Bloom’s:  Understand

AACSB:  Analytical Thinking

48) Estimating the mean from grouped data will tend to be most accurate when

1. A) observations are distributed uniformly within classes.
2. B) there are few classes with wide class limits.
3. C) the sample is not very large and bins are wide.
4. D) the standard deviation is large relative to the mean.

Explanation:  Many bins and uniform data distribution within bins would give a result closest to the ungrouped mean μ.

Difficulty: 1 Easy

Topic:  04.07 Grouped Data

Learning Objective:  04-10 Calculate the mean and standard deviation from grouped data.

Bloom’s:  Apply

AACSB:  Analytical Thinking

49) Which is true of the kurtosis of a distribution?

1. A) A distribution that is flatter than a normal distribution (i.e., thicker tails) is mesokurtic.
2. B) A distribution that is more peaked than a normal distribution (i.e., thinner tails) is platykurtic.
3. C) It is risky to assess kurtosis if the sample size is less than 50.
4. D) The expected range of the kurtosis coefficient increases as n

Explanation:  Shape is hard to judge in small samples. Excel computes kurtosis for samples of any size, but tables of critical values may not go down below n = 50.

Difficulty: 3 Hard

Topic:  04.08 Skewness and Kurtosis

Learning Objective:  04-11 Assess skewness and kurtosis in a sample.

Bloom’s:  Apply

AACSB:  Analytical Thinking

50) Which is true of skewness?

1. A) In business data, positive skewness is unusual.
2. B) In a negatively skewed distribution, the mean is likely to exceed the median.
3. C) Skewness often is evidenced by one or more outliers.
4. D) The expected range of Excel’s skewness coefficient increases as n

Explanation:  Skewness due to extreme data values is common in business data. Right skewness is common, which increases the mean relative to the median.

Difficulty: 3 Hard

Topic:  04.08 Skewness and Kurtosis

Learning Objective:  04-11 Assess skewness and kurtosis in a sample.

Bloom’s:  Apply

AACSB:  Analytical Thinking

51) Which is not true of the Empirical Rule?

1. A) It applies to any distribution.
2. B) It can be applied to fewer distributions than Chebyshev’s Theorem.
3. C) It assumes that the distribution of data follows a bell-shaped, normal curve.
4. D) It predicts more observations within μ ± than Chebyshev’s Theorem.

Explanation:  The Empirical Rule applies only to normal populations, while Chebyshev’s Theorem is general.

Difficulty: 2 Medium

Topic:  04.04 Standardized Data

Learning Objective:  04-05 Apply the Empirical Rule and recognize outliers.

Bloom’s:  Understand

AACSB:  Analytical Thinking

52) Which is a correct statement concerning the median?

1. A) In a left-skewed distribution, we expect that the median will exceed the mean.
2. B) The sum of the deviations around the median is zero.
3. C) The median is an observed data value in any data set.
4. D) The median is halfway between Q1and Q3on a box plot.

Explanation:  The mean is pulled down in left-skewed data, but deviations around it sum to zero in any data set. The median may be between two data values and may not be in the middle of the box plot.

Difficulty: 2 Medium

Topic:  04.02 Measures of Center

Learning Objective:  04-02 Calculate and interpret common measures of center.

Bloom’s:  Apply

AACSB:  Analytical Thinking

53) Which statement is true?

1. A) With nominal data, we can find the mode.
2. B) Outliers distort the mean, but not the standard deviation.
3. C) Business and economic data are rarely skewed to the right.
4. D) If we sample a normal population, the sample skewness coefficient is exactly 0.

Explanation:  The mode (most frequent data value) works for nominal data. Outliers affect both the mean and the standard deviation. Skewness will be near zero in samples from a normal population, but not exactly due to sample variation.

Difficulty: 2 Medium

Topic:  04.02 Measures of Center

Learning Objective:  04-02 Calculate and interpret common measures of center.

Bloom’s:  Understand

AACSB:  Analytical Thinking

54) Exam scores in a small class were 10, 10, 20, 20, 40, 60, 80, 80, 90, 100, 100. For this data set, which statement is incorrect concerning measures of center?

1. A) The median is 60.00.
2. B) The mode is not helpful.
3. C) The 5 percent trimmed mean would be awkward.
4. D) The geometric mean is 35.05.

Explanation:  To find the geometric mean, multiply the data values and take the 11th root to get G = 41.02. Outliers affect both the mean and the standard deviation. There are multiple modes in this example.

Difficulty: 3 Hard

Topic:  04.02 Measures of Center

Learning Objective:  04-02 Calculate and interpret common measures of center.

Bloom’s:  Apply

AACSB:  Analytical Thinking

55) Exam scores in a small class were 0, 50, 50, 70, 70, 80, 90, 90, 100, 100. For this data set, which statement is incorrect concerning measures of center?

1. A) The median is 70.
2. B) The mode is not helpful.
3. C) The geometric mean is useless.
4. D) The mean is 70.

Explanation:  The median is 75 (halfway between x5 = 70 and x6 = 80 in the sorted array). The zeros render the geometric mean useless. The modes in this case are not unique.

Difficulty: 3 Hard

Topic:  04.02 Measures of Center

Learning Objective:  04-02 Calculate and interpret common measures of center.

Bloom’s:  Apply

AACSB:  Analytical Thinking

56) Exam scores in a random sample of students were 0, 50, 50, 70, 70, 80, 90, 90, 90, 100. Which statement is incorrect?

1. A) The standard deviation is 29.61.
2. B) The data are slightly left-skewed.
3. C) The midrange and mean are almost the same.
4. D) The third quartile is 90.

Explanation:  The midrange is (0 + 100)/2 = 50, while the mean is 69. Q3 falls between 90 and 90.

Difficulty: 2 Medium

Topic:  04.02 Measures of Center

Learning Objective:  04-02 Calculate and interpret common measures of center.

Bloom’s:  Apply

AACSB:  Analytical Thinking