HBX Business Analytics Exam | Verified Exam Questions and Answers |
Latest Updated Study Material 2026
Question:
Skewness
Answer:
Skewness measures the degree of a graph's
asymmetry. If the right tail is longer, we say the distribution is skewed to the right or "right-
tailed." Likewise, if the left tail is longer, we say the distribution is skewed to the left or "left-
tailed."
Question:
Rules of thumb for the normal distribution
Answer:
About 68% of the
probability is contained in the range reaching one standard deviation away from the mean on
either side:
Question:
P(?-??x??+?)?68%
About 95% of the probability is contained in the range reaching two standard deviations (1.96 to
be exact) away from the mean on either side:
P(?-2??x??+2?)?95%
We'll use two standard deviations when discussing the normal distribution conceptually, but we
will always use 1.96 for actual calculations in Excel.
About 99.7% of the probability is contained in the range reaching three standard deviations away
from the mean on either side:
P(?-3??x??+3?)?99.7%
Bias
Answer:
avoid biased results by
Question:
- phrasing questions neutrally
-ensuring that the sampling method is appropriate for the demographic of the target population
, -pursuing a high response rate
Normal Distribution
Answer:
a unique symmetrical shape whose centre
and width are determined by its mean and standard deviation respectively.
Question:
z-value
Answer:
the distance that x lies from the mean, measured in
standard deviations.
Question:
Central Limit Theorem
Answer:
If we take enough sufficiently large
samples from any population, the means of those samples will be normally distributed regardless
of the shape of the underlying population.
Question:
Confidence interval
Answer:
An estimate of the range in which the true
population mean likely lies.
Question:
Outlier
Answer:
Technically, a data point is considered an outlier if it is
more than a specified distance below the lower quartile or above the upper quartile of a data set.
Let's start with a couple of definitions. The lower quartile, Q1, is the 25th percentile-by
definition, 25% of all observations fall below Q1. The upper quartile, Q3, is the 75th
percentile-75% of all observations fall below Q3. The interquartile range (IQR) is the
difference between the upper and lower quartiles, that is, IQR=Q3-Q1. We then multiply the IQR
by 1.5 to find the appropriate range, computing 1.5(IQR)=1.5(Q3-Q1). A data point is an outlier
if it is less than Q1-1.5(IQR) or greater than Q3+1.5(IQR).
Latest Updated Study Material 2026
Question:
Skewness
Answer:
Skewness measures the degree of a graph's
asymmetry. If the right tail is longer, we say the distribution is skewed to the right or "right-
tailed." Likewise, if the left tail is longer, we say the distribution is skewed to the left or "left-
tailed."
Question:
Rules of thumb for the normal distribution
Answer:
About 68% of the
probability is contained in the range reaching one standard deviation away from the mean on
either side:
Question:
P(?-??x??+?)?68%
About 95% of the probability is contained in the range reaching two standard deviations (1.96 to
be exact) away from the mean on either side:
P(?-2??x??+2?)?95%
We'll use two standard deviations when discussing the normal distribution conceptually, but we
will always use 1.96 for actual calculations in Excel.
About 99.7% of the probability is contained in the range reaching three standard deviations away
from the mean on either side:
P(?-3??x??+3?)?99.7%
Bias
Answer:
avoid biased results by
Question:
- phrasing questions neutrally
-ensuring that the sampling method is appropriate for the demographic of the target population
, -pursuing a high response rate
Normal Distribution
Answer:
a unique symmetrical shape whose centre
and width are determined by its mean and standard deviation respectively.
Question:
z-value
Answer:
the distance that x lies from the mean, measured in
standard deviations.
Question:
Central Limit Theorem
Answer:
If we take enough sufficiently large
samples from any population, the means of those samples will be normally distributed regardless
of the shape of the underlying population.
Question:
Confidence interval
Answer:
An estimate of the range in which the true
population mean likely lies.
Question:
Outlier
Answer:
Technically, a data point is considered an outlier if it is
more than a specified distance below the lower quartile or above the upper quartile of a data set.
Let's start with a couple of definitions. The lower quartile, Q1, is the 25th percentile-by
definition, 25% of all observations fall below Q1. The upper quartile, Q3, is the 75th
percentile-75% of all observations fall below Q3. The interquartile range (IQR) is the
difference between the upper and lower quartiles, that is, IQR=Q3-Q1. We then multiply the IQR
by 1.5 to find the appropriate range, computing 1.5(IQR)=1.5(Q3-Q1). A data point is an outlier
if it is less than Q1-1.5(IQR) or greater than Q3+1.5(IQR).
