WGU C720 OA LATEST EXAM REAL EXAM 200 QUESTIONS AND
CORRECT ANSWERS (VERIFIED ANSWERS) |AGRADE
1. A company wants to analyze the average monthly sales of its product. Which of the
following is the most appropriate measure of central tendency for this data if there are a
few extremely high sales months due to promotional events?
A) Mean
B) Median
C) Mode
D) Range
E) Standard Deviation
Correct Answer: B) Median
Rationale: The median is less affected by extreme outliers (either very high or very
low values) than the mean, making it a more representative measure of central tendency
for skewed data, such as sales with occasional large promotional spikes.
2. In a normal distribution, approximately what percentage of data falls within one standard
deviation of the mean?
A) 34%
B) 50%
C) 68%
D) 95%
E) 99.7%
Correct Answer: C) 68%
Rationale: According to the empirical rule (or 68-95-99.7 rule), approximately 68% of
data in a normal distribution falls within one standard deviation of the mean.
3. A marketing manager wants to determine if there is a linear relationship between
advertising expenditure and sales revenue. Which statistical tool would be most
appropriate?
A) Hypothesis Testing
B) Confidence Interval
C) Linear Regression
D) Chi-Square Test
E) ANOVA
Correct Answer: C) Linear Regression
Rationale: Linear regression is used to model the relationship between a dependent
variable (sales revenue) and one or more independent variables (advertising
expenditure) to predict future outcomes or understand the strength and direction of
the relationship.
4. A company is deciding whether to launch a new product. They estimate a 60% chance of
success (profit of $500,000) and a 40% chance of failure (loss of $200,000). What is the
, Expected Monetary Value (EMV) of launching the product?
A) $420,000
B) $300,000
C) $220,000
D) $180,000
E) $100,000
Correct Answer: C) $220,000
Rationale: EMV = (Probability of Success * Profit) + (Probability of Failure * Loss). EMV
= (0.60 *
500,000)+(0.40∗−500,000)+(0.40∗−
200,000) = $300,000 - $80,000 = $220,000.
5. Which of the following describes a Type I error in hypothesis testing?
A) Failing to reject a false null hypothesis.
B) Rejecting a true null hypothesis.
C) Failing to reject a true null hypothesis.
D) Rejecting a false null hypothesis.
E) Accepting the alternative hypothesis when it is false.
Correct Answer: B) Rejecting a true null hypothesis.
Rationale: A Type I error occurs when the null hypothesis is true, but we incorrectly
reject it. This is often denoted by the alpha (α) level.
6. A production process consistently produces 3% defective items. If a random sample of
100 items is inspected, what type of probability distribution would be most appropriate
to model the number of defective items?
A) Normal distribution
B) Poisson distribution
C) Binomial distribution
D) Exponential distribution
E) Uniform distribution
Correct Answer: C) Binomial distribution
Rationale: The binomial distribution is appropriate for situations with a fixed number
of trials (100 items), where each trial has only two possible outcomes (defective or not
defective), and the probability of success (defect) is constant (3%).
7. A 95% confidence interval for the average height of adult males in a city is (68 inches, 72
inches). This means that:
A) 95% of adult males in the city have heights between 68 and 72 inches.
B) There is a 95% probability that the true average height of adult males is between 68
and 72 inches.
, C) If we were to take many samples and construct a confidence interval from each,
approximately 95% of these intervals would contain the true population mean.
D) We are 95% confident that any randomly selected adult male will have a height
between 68 and 72 inches.
E) The sample mean height is 70 inches.
Correct Answer: C) If we were to take many samples and construct a confidence
interval from each, approximately 95% of these intervals would contain the true
population mean.
Rationale: The correct interpretation of a confidence interval relates to the reliability
of the estimation process itself, not the probability that a single interval contains the
true mean or the range of individual data points.
8. Which forecasting method relies on averaging past data to smooth out random
fluctuations and highlight trends or seasonal patterns?
A) Exponential Smoothing
B) Linear Regression
C) Qualitative Forecasting
D) Delphi Method
E) Monte Carlo Simulation
Correct Answer: A) Exponential Smoothing
Rationale: Exponential smoothing is a time-series forecasting method that uses a
weighted average of past observations, with more recent observations carrying more
weight. This technique effectively smooths out random variations in data to reveal
underlying patterns.
9. A company wants to minimize the cost of producing two products, A and B, subject to
constraints on labor hours and raw materials. This problem can be solved using:
A) Decision Trees
B) Linear Programming
C) Queuing Theory
D) PERT/CPM
E) Simulation
Correct Answer: B) Linear Programming
Rationale: Linear programming is a mathematical method for determining a way to
achieve the best outcome (such as maximum profit or lowest cost) in a mathematical
model whose requirements are represented by linear relationships.
10. What does a correlation coefficient (r) of +0.9 indicate?
A) A weak negative linear relationship.
B) A strong positive linear relationship.
C) No linear relationship.
, D) A perfect negative linear relationship.
E) A strong positive non-linear relationship.
Correct Answer: B) A strong positive linear relationship.
Rationale: A correlation coefficient close to +1 indicates a strong positive linear
relationship, meaning as one variable increases, the other tends to increase
proportionally.
11. A manufacturing process is known to have a mean weight of 100 grams with a standard
deviation of 5 grams. If the weights are normally distributed, what is the probability of a
randomly selected item weighing more than 105 grams?
A) 50%
B) 34%
C) 16%
D) 5%
E) 2.5%
Correct Answer: C) 16%
Rationale: In a normal distribution, 68% of data is within ±1 standard deviation. This
means 34% is between the mean and +1 standard deviation (100-105g). Since the
distribution is symmetric, 50% of the data is above the mean. Therefore, the probability
of weighing more than 105 grams is 50% - 34% = 16%.
12. Which of the following is a qualitative forecasting method?
A) Moving Average
B) Exponential Smoothing
C) Delphi Method
D) Trend Projection
E) ARIMA Models
Correct Answer: C) Delphi Method
Rationale: The Delphi method is a structured communication technique, originally
developed as a systematic, interactive forecasting method that relies on a panel of
experts. It's qualitative because it uses expert judgment rather than historical numerical
data.
13. A restaurant owner wants to compare the average waiting times at three different
branches to see if there's a statistically significant difference. Which test should be used?
A) Z-test
B) t-test
C) ANOVA (Analysis of Variance)
D) Chi-Square Test
E) Correlation Analysis
Correct Answer: C) ANOVA (Analysis of Variance)
CORRECT ANSWERS (VERIFIED ANSWERS) |AGRADE
1. A company wants to analyze the average monthly sales of its product. Which of the
following is the most appropriate measure of central tendency for this data if there are a
few extremely high sales months due to promotional events?
A) Mean
B) Median
C) Mode
D) Range
E) Standard Deviation
Correct Answer: B) Median
Rationale: The median is less affected by extreme outliers (either very high or very
low values) than the mean, making it a more representative measure of central tendency
for skewed data, such as sales with occasional large promotional spikes.
2. In a normal distribution, approximately what percentage of data falls within one standard
deviation of the mean?
A) 34%
B) 50%
C) 68%
D) 95%
E) 99.7%
Correct Answer: C) 68%
Rationale: According to the empirical rule (or 68-95-99.7 rule), approximately 68% of
data in a normal distribution falls within one standard deviation of the mean.
3. A marketing manager wants to determine if there is a linear relationship between
advertising expenditure and sales revenue. Which statistical tool would be most
appropriate?
A) Hypothesis Testing
B) Confidence Interval
C) Linear Regression
D) Chi-Square Test
E) ANOVA
Correct Answer: C) Linear Regression
Rationale: Linear regression is used to model the relationship between a dependent
variable (sales revenue) and one or more independent variables (advertising
expenditure) to predict future outcomes or understand the strength and direction of
the relationship.
4. A company is deciding whether to launch a new product. They estimate a 60% chance of
success (profit of $500,000) and a 40% chance of failure (loss of $200,000). What is the
, Expected Monetary Value (EMV) of launching the product?
A) $420,000
B) $300,000
C) $220,000
D) $180,000
E) $100,000
Correct Answer: C) $220,000
Rationale: EMV = (Probability of Success * Profit) + (Probability of Failure * Loss). EMV
= (0.60 *
500,000)+(0.40∗−500,000)+(0.40∗−
200,000) = $300,000 - $80,000 = $220,000.
5. Which of the following describes a Type I error in hypothesis testing?
A) Failing to reject a false null hypothesis.
B) Rejecting a true null hypothesis.
C) Failing to reject a true null hypothesis.
D) Rejecting a false null hypothesis.
E) Accepting the alternative hypothesis when it is false.
Correct Answer: B) Rejecting a true null hypothesis.
Rationale: A Type I error occurs when the null hypothesis is true, but we incorrectly
reject it. This is often denoted by the alpha (α) level.
6. A production process consistently produces 3% defective items. If a random sample of
100 items is inspected, what type of probability distribution would be most appropriate
to model the number of defective items?
A) Normal distribution
B) Poisson distribution
C) Binomial distribution
D) Exponential distribution
E) Uniform distribution
Correct Answer: C) Binomial distribution
Rationale: The binomial distribution is appropriate for situations with a fixed number
of trials (100 items), where each trial has only two possible outcomes (defective or not
defective), and the probability of success (defect) is constant (3%).
7. A 95% confidence interval for the average height of adult males in a city is (68 inches, 72
inches). This means that:
A) 95% of adult males in the city have heights between 68 and 72 inches.
B) There is a 95% probability that the true average height of adult males is between 68
and 72 inches.
, C) If we were to take many samples and construct a confidence interval from each,
approximately 95% of these intervals would contain the true population mean.
D) We are 95% confident that any randomly selected adult male will have a height
between 68 and 72 inches.
E) The sample mean height is 70 inches.
Correct Answer: C) If we were to take many samples and construct a confidence
interval from each, approximately 95% of these intervals would contain the true
population mean.
Rationale: The correct interpretation of a confidence interval relates to the reliability
of the estimation process itself, not the probability that a single interval contains the
true mean or the range of individual data points.
8. Which forecasting method relies on averaging past data to smooth out random
fluctuations and highlight trends or seasonal patterns?
A) Exponential Smoothing
B) Linear Regression
C) Qualitative Forecasting
D) Delphi Method
E) Monte Carlo Simulation
Correct Answer: A) Exponential Smoothing
Rationale: Exponential smoothing is a time-series forecasting method that uses a
weighted average of past observations, with more recent observations carrying more
weight. This technique effectively smooths out random variations in data to reveal
underlying patterns.
9. A company wants to minimize the cost of producing two products, A and B, subject to
constraints on labor hours and raw materials. This problem can be solved using:
A) Decision Trees
B) Linear Programming
C) Queuing Theory
D) PERT/CPM
E) Simulation
Correct Answer: B) Linear Programming
Rationale: Linear programming is a mathematical method for determining a way to
achieve the best outcome (such as maximum profit or lowest cost) in a mathematical
model whose requirements are represented by linear relationships.
10. What does a correlation coefficient (r) of +0.9 indicate?
A) A weak negative linear relationship.
B) A strong positive linear relationship.
C) No linear relationship.
, D) A perfect negative linear relationship.
E) A strong positive non-linear relationship.
Correct Answer: B) A strong positive linear relationship.
Rationale: A correlation coefficient close to +1 indicates a strong positive linear
relationship, meaning as one variable increases, the other tends to increase
proportionally.
11. A manufacturing process is known to have a mean weight of 100 grams with a standard
deviation of 5 grams. If the weights are normally distributed, what is the probability of a
randomly selected item weighing more than 105 grams?
A) 50%
B) 34%
C) 16%
D) 5%
E) 2.5%
Correct Answer: C) 16%
Rationale: In a normal distribution, 68% of data is within ±1 standard deviation. This
means 34% is between the mean and +1 standard deviation (100-105g). Since the
distribution is symmetric, 50% of the data is above the mean. Therefore, the probability
of weighing more than 105 grams is 50% - 34% = 16%.
12. Which of the following is a qualitative forecasting method?
A) Moving Average
B) Exponential Smoothing
C) Delphi Method
D) Trend Projection
E) ARIMA Models
Correct Answer: C) Delphi Method
Rationale: The Delphi method is a structured communication technique, originally
developed as a systematic, interactive forecasting method that relies on a panel of
experts. It's qualitative because it uses expert judgment rather than historical numerical
data.
13. A restaurant owner wants to compare the average waiting times at three different
branches to see if there's a statistically significant difference. Which test should be used?
A) Z-test
B) t-test
C) ANOVA (Analysis of Variance)
D) Chi-Square Test
E) Correlation Analysis
Correct Answer: C) ANOVA (Analysis of Variance)
