WGU D772 ACTUAL EXAM PAPER 2026
QUESTIONS WITH ANSWERS GRADED A+
◍ Quartiles.
Answer: values that divide an ordered data set into four equal parts. They
are special percentiles that help to understand the spread and center of the
data.
◍ Positive correlation.
Answer: dots slope upward
◍ C→Q Role-type Classification.
Answer: When we have a C→Q (Categorical to Quantitative) role-type
classification, a side-by-side boxplot
◍ disjoint events (mutually exclusive):.
Answer: events that cannot occur at the same time
◍ Histogram.
Answer: is a chart which displays quantitative data by grouping it into
intervals (also called bins or classes) of a set width on the x-axis. A bar is
placed above each interval and its height represents the number of data
points that fall in that interval, called the frequency or count.
◍ The variable that is affected is called.
Answer: Response variable
◍ Outlier.
Answer: Any single point that is significantly separated from the pattern is
called an
◍ IQR.
Answer: Q3-Q1
,◍ Observational Studies.
Answer: researchers observe and record data on variables as they naturally
occur, without any intervention or manipulation
◍ The Complement Rule.
Answer: P(not A) = 1 − P(A)
◍ categorical data.
Answer: Data that uses names labels or categories to group things. Ex. hair
color, address.
◍ Dependent Events.
Answer: Two events are dependent if the occurrence of one affects the
probability of the other.
◍ Scatterplot.
Answer: q->q
◍ Systematic sampling.
Answer: involves selecting every "nth" individual from a list of the
population, starting from a randomly chosen point. This method is often
used when it's easy to access a list of the entire population. It's a simple and
efficient method
◍ Theoretical probability.
Answer: is generally used in situations where the structure of the scenario is
assumed to dictate the likelihood of the outcomes. Examples of this include
flipping coins, rolling dice, spinning spinners, lotteries, raffles, and
drawings.
◍ Role-type classification.
Answer: Categorical Explanatory and Categorical Response (C →
C)Categorical Explanatory and Quantitative Response (C → Q)Quantitative
Explanatory and Quantitative Response (Q → Q)Quantitative Explanatory
and Categorical Response (Q → C
◍ They might receive no treatment, or they may receive what is referred to as
, a.
Answer: placebo
◍ statistically significant.
Answer: it is unlikely that the result we measured differed from the expected
result solely because of random chance
◍ Strong correlation.
Answer: Dots follow a pattern tightly
◍ C→C Role-type Classification.
Answer: When we have a C→C (Categorical to Categorical) role-type
classification, a two-way table
◍ Association.
Answer: the two variables are related in some way, but this relationship may
not be direct, and there may be no cause-and-effect relationship between
them.
◍ Confounding variable (lurking variable).
Answer: a variable that is not accounted for, but that may influence the
relationship being studied because it is associated with both the explanatory
and response variable.
◍ Cluster (Example).
Answer: A researcher studying the opinions of high school students in a
state might randomly select five schools and survey all students within those
schools.
◍ Unlikely.
Answer: less than 40%, but greater than 0%
◍ Venn diagram.
Answer: a visual tool that can be used to represent the outcomes of an
experiment
◍ Mode.
Answer: the most frequent value.
QUESTIONS WITH ANSWERS GRADED A+
◍ Quartiles.
Answer: values that divide an ordered data set into four equal parts. They
are special percentiles that help to understand the spread and center of the
data.
◍ Positive correlation.
Answer: dots slope upward
◍ C→Q Role-type Classification.
Answer: When we have a C→Q (Categorical to Quantitative) role-type
classification, a side-by-side boxplot
◍ disjoint events (mutually exclusive):.
Answer: events that cannot occur at the same time
◍ Histogram.
Answer: is a chart which displays quantitative data by grouping it into
intervals (also called bins or classes) of a set width on the x-axis. A bar is
placed above each interval and its height represents the number of data
points that fall in that interval, called the frequency or count.
◍ The variable that is affected is called.
Answer: Response variable
◍ Outlier.
Answer: Any single point that is significantly separated from the pattern is
called an
◍ IQR.
Answer: Q3-Q1
,◍ Observational Studies.
Answer: researchers observe and record data on variables as they naturally
occur, without any intervention or manipulation
◍ The Complement Rule.
Answer: P(not A) = 1 − P(A)
◍ categorical data.
Answer: Data that uses names labels or categories to group things. Ex. hair
color, address.
◍ Dependent Events.
Answer: Two events are dependent if the occurrence of one affects the
probability of the other.
◍ Scatterplot.
Answer: q->q
◍ Systematic sampling.
Answer: involves selecting every "nth" individual from a list of the
population, starting from a randomly chosen point. This method is often
used when it's easy to access a list of the entire population. It's a simple and
efficient method
◍ Theoretical probability.
Answer: is generally used in situations where the structure of the scenario is
assumed to dictate the likelihood of the outcomes. Examples of this include
flipping coins, rolling dice, spinning spinners, lotteries, raffles, and
drawings.
◍ Role-type classification.
Answer: Categorical Explanatory and Categorical Response (C →
C)Categorical Explanatory and Quantitative Response (C → Q)Quantitative
Explanatory and Quantitative Response (Q → Q)Quantitative Explanatory
and Categorical Response (Q → C
◍ They might receive no treatment, or they may receive what is referred to as
, a.
Answer: placebo
◍ statistically significant.
Answer: it is unlikely that the result we measured differed from the expected
result solely because of random chance
◍ Strong correlation.
Answer: Dots follow a pattern tightly
◍ C→C Role-type Classification.
Answer: When we have a C→C (Categorical to Categorical) role-type
classification, a two-way table
◍ Association.
Answer: the two variables are related in some way, but this relationship may
not be direct, and there may be no cause-and-effect relationship between
them.
◍ Confounding variable (lurking variable).
Answer: a variable that is not accounted for, but that may influence the
relationship being studied because it is associated with both the explanatory
and response variable.
◍ Cluster (Example).
Answer: A researcher studying the opinions of high school students in a
state might randomly select five schools and survey all students within those
schools.
◍ Unlikely.
Answer: less than 40%, but greater than 0%
◍ Venn diagram.
Answer: a visual tool that can be used to represent the outcomes of an
experiment
◍ Mode.
Answer: the most frequent value.
