BARBER PRACTICE STATE BOARD
ACTUAL EXAM PAPER 2026
QUESTIONS WITH ANSWERS GRADED
A+
◍ What do descriptive questions ask?.
Answer: What happened? (e.g., which customers are most alike)
◍ Factor Based Models.
Answer: classification, clustering, regression. Implicitly assumed that we
have a lot of factors in the final model
◍ Why limit number of factors in a model? 2 reasons.
Answer: overfitting: when # of factors is close to or larger than # of data
points. Model may fit too closely to random effectssimplicity: simple
models are usually better
◍ Classical variable selection approaches.
Answer: 1. Forward selection2. Backwards elimination3. Stepwise
regressiongreedy algorithms
◍ Backward elimination.
Answer: variable selection; classicalOpposite of forward selection. Start
with model with all factors, at each step find worst factor and remove from
model. Continue until no more to add, # of factor threshold is satisfied.
Remove factors at the end that were not good enough
◍ Forward selection.
Answer: variable selection; classicalStart with model with no factors, at each
step find best new factor to add. Continue until none bad enough to remove,
# of factor threshold is satisfied. Remove factors at the end that were not
, good enough
◍ Stepwise regression.
Answer: variable selection; classicalCombination of forward selection and
backwards elimination. Start with all or no factors. Each step remove/add a
factor. As it continues, after adding in new factor we eliminate right away
any factors that may be good. Helps model adjust when new factors are
added, goodness values change
◍ What do predictive questions ask?.
Answer: What will happen? (e.g., what will Google's stock price be?)
◍ What do prescriptive questions ask?.
Answer: What action(s) would be best? (e.g., where to put traffic lights)
◍ Ways of determining if factors are good enough in variable selection.
Answer: p-value, Rsquared, AIC, BIC
◍ Greedy algorithm.
Answer: At each step, it does the one thing that looks bestwithout taking
future options into consideration. Good for initial analysis1. Forward
selection2. Backwards elimination3. Stepwise regression
◍ Global variable selection approaches.
Answer: 1. LASSO2. Elastic NetSlower, but tend to give better predictive
models
◍ What is a model?.
Answer: Real-life situation expressed as math.
◍ What do classifiers help you do?.
Answer: differentiate
◍ LASSO.
Answer: variable selection; global- SCALE the date (as with any
constrained sum of coefficients)- add a constraint to the standard regression
equation- minimize sum of squared errors- T = limit or "budget" on how
large the sum of squared errors can get. Budget will be used on most
, important coefficients- Method for limiting the number of variables in a
model by limiting the sum of all coefficients’ absolute values. Can be very
helpful when number of data points is less than number of factors.
◍ Elastic Net.
Answer: variable selection; global- SCALE the date (as with any
constrained sum of coefficients)- T = limit or "budget" on how large the sum
of squared errors can get. Budget will be used on most important
coefficients- Combination of lasso and ridge regression.- Variable selection
benefits of LASSO- Predictive benefits of ridge regression
◍ Ridge Regression.
Answer: - Method of regularization by limiting the sum of the squares of the
coefficients. Will reduce the magnitude of coefficients, not the number of
variables chosen.- The quadratic term in ridge regressiontends to shrink the
coefficient values i.e Whatever the basic regression model coefficients
would be,the quadratic constraint pushes them toward zeroor regularizes
them.
◍ What is a soft classifier and when is it used?.
Answer: In some cases, there won't be a line that separates all of the labeled
examples. So we use a classifier that minimizes the number of mistakes.
◍ Design of Experiments (DOE).
Answer: How can we still have a representative sample of each combination
of factors, while only surveying 600 people?How to determine which of the
several factors are mostimportant to predicting someone's
answers?comparison to measure differencecontrol for other factors and
effectsblocking factors that account for the variation between factors (red
sports car vs red minivan example)
◍ What does it mean when the classifier/decision boundary is almost parallel
to the vertical x-axis?.
Answer: The horizontal attribute is all that is needed.
◍ What does it mean when the classifier/decision boundary is almost parallel
, to the horizontal y-axis?.
Answer: The vertical attribute is all that is needed.
◍ A/B testing.
Answer: Whenever we want to choose between 2 alternatives.As long as the
following 3 things are true:1st, we need to be able to collect a lot of data
quickly enough to get an answer in time to use it.2nd, the data we collect has
to be from a representative sample of the whole3rd, the amount of data we
collect has to be small compared to the total population we want to use the
answer on.Before modeling and before collecting data
◍ What is time-series data?.
Answer: The same data recorded over time often recorded at equal intervals
◍ What is quantitative data?.
Answer: Number with a meaning: higher means more, lower means less
(e.g., age, sales, temperature, income)
◍ What is categorical data?.
Answer: Numbers w/o meaning (e.g., zip codes), non-numeric (e.g., hair
color), binary data (e.g., male/female, yes/no, on/off)
◍ (Full) Factorial Design.
Answer: Test every combination of variables in an experiment to find each
one's effect, and interaction effects on the outcome.
◍ Fractional Factorial Design.
Answer: A subset of combinations to test - selected combinations give same
result as full factorial design i.e a balanced designBefore modeling and
before collecting data
◍ Which of these is time series data?
A. The average cost of a house in the United States every year since 1820
B. The height of each professional basketball player in the NBA at the start
of the season.
Answer: A
ACTUAL EXAM PAPER 2026
QUESTIONS WITH ANSWERS GRADED
A+
◍ What do descriptive questions ask?.
Answer: What happened? (e.g., which customers are most alike)
◍ Factor Based Models.
Answer: classification, clustering, regression. Implicitly assumed that we
have a lot of factors in the final model
◍ Why limit number of factors in a model? 2 reasons.
Answer: overfitting: when # of factors is close to or larger than # of data
points. Model may fit too closely to random effectssimplicity: simple
models are usually better
◍ Classical variable selection approaches.
Answer: 1. Forward selection2. Backwards elimination3. Stepwise
regressiongreedy algorithms
◍ Backward elimination.
Answer: variable selection; classicalOpposite of forward selection. Start
with model with all factors, at each step find worst factor and remove from
model. Continue until no more to add, # of factor threshold is satisfied.
Remove factors at the end that were not good enough
◍ Forward selection.
Answer: variable selection; classicalStart with model with no factors, at each
step find best new factor to add. Continue until none bad enough to remove,
# of factor threshold is satisfied. Remove factors at the end that were not
, good enough
◍ Stepwise regression.
Answer: variable selection; classicalCombination of forward selection and
backwards elimination. Start with all or no factors. Each step remove/add a
factor. As it continues, after adding in new factor we eliminate right away
any factors that may be good. Helps model adjust when new factors are
added, goodness values change
◍ What do predictive questions ask?.
Answer: What will happen? (e.g., what will Google's stock price be?)
◍ What do prescriptive questions ask?.
Answer: What action(s) would be best? (e.g., where to put traffic lights)
◍ Ways of determining if factors are good enough in variable selection.
Answer: p-value, Rsquared, AIC, BIC
◍ Greedy algorithm.
Answer: At each step, it does the one thing that looks bestwithout taking
future options into consideration. Good for initial analysis1. Forward
selection2. Backwards elimination3. Stepwise regression
◍ Global variable selection approaches.
Answer: 1. LASSO2. Elastic NetSlower, but tend to give better predictive
models
◍ What is a model?.
Answer: Real-life situation expressed as math.
◍ What do classifiers help you do?.
Answer: differentiate
◍ LASSO.
Answer: variable selection; global- SCALE the date (as with any
constrained sum of coefficients)- add a constraint to the standard regression
equation- minimize sum of squared errors- T = limit or "budget" on how
large the sum of squared errors can get. Budget will be used on most
, important coefficients- Method for limiting the number of variables in a
model by limiting the sum of all coefficients’ absolute values. Can be very
helpful when number of data points is less than number of factors.
◍ Elastic Net.
Answer: variable selection; global- SCALE the date (as with any
constrained sum of coefficients)- T = limit or "budget" on how large the sum
of squared errors can get. Budget will be used on most important
coefficients- Combination of lasso and ridge regression.- Variable selection
benefits of LASSO- Predictive benefits of ridge regression
◍ Ridge Regression.
Answer: - Method of regularization by limiting the sum of the squares of the
coefficients. Will reduce the magnitude of coefficients, not the number of
variables chosen.- The quadratic term in ridge regressiontends to shrink the
coefficient values i.e Whatever the basic regression model coefficients
would be,the quadratic constraint pushes them toward zeroor regularizes
them.
◍ What is a soft classifier and when is it used?.
Answer: In some cases, there won't be a line that separates all of the labeled
examples. So we use a classifier that minimizes the number of mistakes.
◍ Design of Experiments (DOE).
Answer: How can we still have a representative sample of each combination
of factors, while only surveying 600 people?How to determine which of the
several factors are mostimportant to predicting someone's
answers?comparison to measure differencecontrol for other factors and
effectsblocking factors that account for the variation between factors (red
sports car vs red minivan example)
◍ What does it mean when the classifier/decision boundary is almost parallel
to the vertical x-axis?.
Answer: The horizontal attribute is all that is needed.
◍ What does it mean when the classifier/decision boundary is almost parallel
, to the horizontal y-axis?.
Answer: The vertical attribute is all that is needed.
◍ A/B testing.
Answer: Whenever we want to choose between 2 alternatives.As long as the
following 3 things are true:1st, we need to be able to collect a lot of data
quickly enough to get an answer in time to use it.2nd, the data we collect has
to be from a representative sample of the whole3rd, the amount of data we
collect has to be small compared to the total population we want to use the
answer on.Before modeling and before collecting data
◍ What is time-series data?.
Answer: The same data recorded over time often recorded at equal intervals
◍ What is quantitative data?.
Answer: Number with a meaning: higher means more, lower means less
(e.g., age, sales, temperature, income)
◍ What is categorical data?.
Answer: Numbers w/o meaning (e.g., zip codes), non-numeric (e.g., hair
color), binary data (e.g., male/female, yes/no, on/off)
◍ (Full) Factorial Design.
Answer: Test every combination of variables in an experiment to find each
one's effect, and interaction effects on the outcome.
◍ Fractional Factorial Design.
Answer: A subset of combinations to test - selected combinations give same
result as full factorial design i.e a balanced designBefore modeling and
before collecting data
◍ Which of these is time series data?
A. The average cost of a house in the United States every year since 1820
B. The height of each professional basketball player in the NBA at the start
of the season.
Answer: A
