ENGINEERING AND THE SCIENCES LEARNING
WORKBOOK 2026 RANDOM VARIABLES AND
DISTRIBUTIONS
◉ What are the basic steps to build a frequency distribution?
Answer: 1. Find the minimum and maximum values in the data set.
2. Determine class intervals that cover the range between the
minimum and maximum without overlapping. 3. Find frequency: the
number of observations in the data that belong to each class
interval.
◉ What is the purpose of measures of central tendency? Answer:
Measures of central tendency are used to summarize a set of data by
identifying the central point within that dataset, typically through
the mean, median, and mode.
◉ What are measures of variation? Answer: Measures of variation
describe the spread or dispersion of a dataset, indicating how much
the data points differ from each other, commonly including range,
variance, and standard deviation.
◉ What is the relationship between two variables? Answer: The
relationship between two variables refers to how one variable
,changes in relation to another, which can be analyzed using
correlation and regression techniques.
◉ What is probability? Answer: Probability is a measure of the
likelihood that an event will occur, expressed as a number between 0
(impossible event) and 1 (certain event).
◉ What is a sample space? Answer: A sample space is the set of all
possible outcomes of a random experiment.
◉ What is conditional probability? Answer: Conditional probability
is the probability of an event occurring given that another event has
already occurred.
◉ What is Bayes' Theorem? Answer: Bayes' Theorem is a
mathematical formula that describes how to update the probability
of a hypothesis based on new evidence.
◉ What is a random variable? Answer: A random variable is a
numerical outcome of a random phenomenon, which can be discrete
or continuous.
◉ What is a probability distribution? Answer: A probability
distribution describes how the probabilities are distributed over the
values of the random variable.
,◉ What is the mean of discrete random variables? Answer: The
mean of discrete random variables is the expected value, calculated
as the sum of all possible values, each multiplied by its probability.
◉ What is the binomial distribution? Answer: The binomial
distribution is a discrete probability distribution that models the
number of successes in a fixed number of independent Bernoulli
trials.
◉ What is the Poisson distribution? Answer: The Poisson
distribution is a discrete probability distribution that expresses the
probability of a given number of events occurring in a fixed interval
of time or space.
◉ What is the normal distribution? Answer: The normal distribution
is a continuous probability distribution characterized by a
symmetric bell-shaped curve, defined by its mean and standard
deviation.
◉ What is the Central Limit Theorem? Answer: The Central Limit
Theorem states that the sampling distribution of the sample mean
approaches a normal distribution as the sample size increases,
regardless of the population's distribution.
, ◉ What is point estimation? Answer: Point estimation is the process
of providing a single value estimate of a population parameter based
on sample data.
◉ What is hypothesis testing? Answer: Hypothesis testing is a
statistical method used to make decisions about population
parameters based on sample data, involving formulating null and
alternative hypotheses.
◉ What is a confidence interval? Answer: A confidence interval is a
range of values, derived from sample statistics, that is likely to
contain the value of an unknown population parameter.
◉ What is the difference between one-sample and two-sample
inferences? Answer: One-sample inferences involve making
conclusions about a population based on a single sample, while two-
sample inferences compare two different populations based on two
separate samples.
◉ What is the purpose of statistical tests? Answer: Statistical tests
are used to determine if there is enough evidence to reject a null
hypothesis in favor of an alternative hypothesis.
◉ What is variance? Answer: Variance is a measure of how much the
values in a dataset differ from the mean, calculated as the average of
the squared differences from the mean.