MSIS 3223 Exam 2 Practice Examination
Comprehensive Preparation for Chapters 5, 8, and 9
This comprehensive practice examination is designed to prepare MIS and business statistics
students for Exam 2, covering probability distributions and random variables (Chapter 5),
regression analysis techniques (Chapter 8), and forecasting methods (Chapter 9). The exam
aligns with standard business statistics curriculum and emphasizes practical applications using
Excel data analysis tools, mathematical modeling, and statistical reasoning essential for data-
driven decision making in management information systems.
CHAPTER 5: PROBABILITY & RANDOM VARIABLES (Questions 1-30)
Probability Definitions & Random Variables
Question 1: Which of the following is true about variance?
A. It measures the central tendency of a random variable
B. It measures the uncertainty of a random variable
C. It is always less than the expected value
D. It is the square root of the standard deviation
Answer: B
Rationale: Variance measures the dispersion or spread of a random variable's possible values
around its mean. It quantifies the uncertainty associated with the random variable; the higher
the variance, the greater the uncertainty of the outcome.
Question 2: A probability density function:
A. Characterizes outcomes of a discrete random variable
B. Characterizes outcomes of a continuous random variable
C. Only applies to binomial distributions
D. Measures only the mean of a distribution
Answer: B
Rationale: A probability density function (PDF) characterizes the outcomes of a continuous
random variable. The total area under the density function above the x-axis equals 1, and it
calculates the probability of a random variable lying within a certain interval.
,Question 3: If the process that generates the outcome is known, probabilities can be deduced
from theoretical arguments. This is the _____ definition of probability.
A. Relative frequency
B. Subjective
C. Classical
D. Empirical
Answer: C
Rationale: The classical definition of probability applies when the process generating outcomes
is known and outcomes are equally likely. Examples include rolling dice or drawing cards, where
probabilities can be deduced from theoretical arguments rather than empirical data.
Question 4: The _____ definition of probability is based on empirical data. The probability that
an outcome will occur is simply the relative frequency associated with that outcome.
A. Classical
B. Subjective
C. A priori
D. Relative frequency
Answer: D
Rationale: The relative frequency definition of probability is based on observed data from
experiments or historical records. The probability of an outcome is calculated as the number of
times the outcome occurs divided by the total number of trials.
Question 5: The _____ definition of probability is based on judgment and experience, such as
when sports experts predict the probability of a specific team winning a championship.
A. Classical
B. Relative frequency
C. Subjective
D. Objective
Answer: C
,Rationale: Subjective probability is based on an individual's judgment, experience, and intuition
rather than on theoretical calculations or empirical data. It is often used when historical data is
not available or when forecasting unique events.
Question 6: A _____ is a numerical description of the outcome of an experiment. Random
variables may be continuous or discrete.
A. Probability distribution
B. Random variable
C. Probability density function
D. Variance
Answer: B
Rationale: A random variable assigns a numerical value to each outcome of an experiment.
Random variables can be discrete (countable outcomes, e.g., number of customers) or
continuous (outcomes over an interval, e.g., time or weight).
Question 7: A _____ is a random variable for which the number of possible outcomes can be
counted.
A. Continuous random variable
B. Discrete random variable
C. Normal random variable
D. Binomial random variable
Answer: B
Rationale: A discrete random variable has a countable number of possible outcomes (finite or
countably infinite). Examples include the number of defective items in a sample, the number of
customers in a queue, or the roll of a die.
Question 8: A _____ is a random variable that has outcomes over one or more continuous
intervals or real numbers.
A. Discrete random variable
B. Continuous random variable
C. Bernoulli random variable
D. Poisson random variable
, Answer: B
Rationale: A continuous random variable can take any value within a specified interval or range.
Examples include height, weight, temperature, or time. The probability of any single exact value
is zero; probabilities are calculated over intervals.
Question 9: The _____ of a random variable corresponds to the notion of the mean, or average,
for a sample. It can be helpful in making a variety of decisions.
A. Variance
B. Standard deviation
C. Expected value
D. Probability
Answer: C
Rationale: The expected value (or mean) of a random variable is the weighted average of all
possible outcomes, where weights are the probabilities of each outcome. It represents the long-
run average value if the experiment is repeated many times.
Question 10: Random variables may be continuous or discrete.
A. True
B. False
Answer: A
Rationale: Random variables are classified as either discrete (countable outcomes) or
continuous (outcomes over intervals). This fundamental distinction determines which
probability distributions and analysis methods are appropriate.
Probability Distributions
Question 11: The variance of a discrete random variable may be computed as a _____ from the
expected value. It is a common measure of dispersion.
A. Simple average of the deviations
B. Weighted average of the squared deviations
C. Sum of the absolute deviations
D. Product of the deviations
Comprehensive Preparation for Chapters 5, 8, and 9
This comprehensive practice examination is designed to prepare MIS and business statistics
students for Exam 2, covering probability distributions and random variables (Chapter 5),
regression analysis techniques (Chapter 8), and forecasting methods (Chapter 9). The exam
aligns with standard business statistics curriculum and emphasizes practical applications using
Excel data analysis tools, mathematical modeling, and statistical reasoning essential for data-
driven decision making in management information systems.
CHAPTER 5: PROBABILITY & RANDOM VARIABLES (Questions 1-30)
Probability Definitions & Random Variables
Question 1: Which of the following is true about variance?
A. It measures the central tendency of a random variable
B. It measures the uncertainty of a random variable
C. It is always less than the expected value
D. It is the square root of the standard deviation
Answer: B
Rationale: Variance measures the dispersion or spread of a random variable's possible values
around its mean. It quantifies the uncertainty associated with the random variable; the higher
the variance, the greater the uncertainty of the outcome.
Question 2: A probability density function:
A. Characterizes outcomes of a discrete random variable
B. Characterizes outcomes of a continuous random variable
C. Only applies to binomial distributions
D. Measures only the mean of a distribution
Answer: B
Rationale: A probability density function (PDF) characterizes the outcomes of a continuous
random variable. The total area under the density function above the x-axis equals 1, and it
calculates the probability of a random variable lying within a certain interval.
,Question 3: If the process that generates the outcome is known, probabilities can be deduced
from theoretical arguments. This is the _____ definition of probability.
A. Relative frequency
B. Subjective
C. Classical
D. Empirical
Answer: C
Rationale: The classical definition of probability applies when the process generating outcomes
is known and outcomes are equally likely. Examples include rolling dice or drawing cards, where
probabilities can be deduced from theoretical arguments rather than empirical data.
Question 4: The _____ definition of probability is based on empirical data. The probability that
an outcome will occur is simply the relative frequency associated with that outcome.
A. Classical
B. Subjective
C. A priori
D. Relative frequency
Answer: D
Rationale: The relative frequency definition of probability is based on observed data from
experiments or historical records. The probability of an outcome is calculated as the number of
times the outcome occurs divided by the total number of trials.
Question 5: The _____ definition of probability is based on judgment and experience, such as
when sports experts predict the probability of a specific team winning a championship.
A. Classical
B. Relative frequency
C. Subjective
D. Objective
Answer: C
,Rationale: Subjective probability is based on an individual's judgment, experience, and intuition
rather than on theoretical calculations or empirical data. It is often used when historical data is
not available or when forecasting unique events.
Question 6: A _____ is a numerical description of the outcome of an experiment. Random
variables may be continuous or discrete.
A. Probability distribution
B. Random variable
C. Probability density function
D. Variance
Answer: B
Rationale: A random variable assigns a numerical value to each outcome of an experiment.
Random variables can be discrete (countable outcomes, e.g., number of customers) or
continuous (outcomes over an interval, e.g., time or weight).
Question 7: A _____ is a random variable for which the number of possible outcomes can be
counted.
A. Continuous random variable
B. Discrete random variable
C. Normal random variable
D. Binomial random variable
Answer: B
Rationale: A discrete random variable has a countable number of possible outcomes (finite or
countably infinite). Examples include the number of defective items in a sample, the number of
customers in a queue, or the roll of a die.
Question 8: A _____ is a random variable that has outcomes over one or more continuous
intervals or real numbers.
A. Discrete random variable
B. Continuous random variable
C. Bernoulli random variable
D. Poisson random variable
, Answer: B
Rationale: A continuous random variable can take any value within a specified interval or range.
Examples include height, weight, temperature, or time. The probability of any single exact value
is zero; probabilities are calculated over intervals.
Question 9: The _____ of a random variable corresponds to the notion of the mean, or average,
for a sample. It can be helpful in making a variety of decisions.
A. Variance
B. Standard deviation
C. Expected value
D. Probability
Answer: C
Rationale: The expected value (or mean) of a random variable is the weighted average of all
possible outcomes, where weights are the probabilities of each outcome. It represents the long-
run average value if the experiment is repeated many times.
Question 10: Random variables may be continuous or discrete.
A. True
B. False
Answer: A
Rationale: Random variables are classified as either discrete (countable outcomes) or
continuous (outcomes over intervals). This fundamental distinction determines which
probability distributions and analysis methods are appropriate.
Probability Distributions
Question 11: The variance of a discrete random variable may be computed as a _____ from the
expected value. It is a common measure of dispersion.
A. Simple average of the deviations
B. Weighted average of the squared deviations
C. Sum of the absolute deviations
D. Product of the deviations
