BARBER PRACTICE STATE BOARD
FINAL TEST 2026 QUESTIONS WITH
CORRECT ANSWERS GRADED A+
◍ Rows.
Answer: Data points are values in data tables
◍ when might overfitting occur.
Answer: when the # of factors is close to or larger than the # of data points
causing the model to potentially fit too closely to random effects
◍ Why are simple models better than complex ones.
Answer: less data is required; less chance of insignificant factors and easier
to interpret
◍ Columns.
Answer: The 'answer' for each data point (response/outcome)
◍ what is forward selection.
Answer: we select the best new factor and see if it's good enough (R^2, AIC,
or p-value) add it to our model and fit the model with the current set of
factors. Then at the end we remove factors that are lower than a certain
threshold
◍ what is backward elimination.
Answer: we start with all factors and find the worst on a supplied threshold
(p = 0.15). If it is worse we remove it and start the process over. We do that
until we have the number of factors that we want and then we move the
factors lower than a second threshold (p = .05) and fit the model with all set
of factors
◍ what is stepwise regression.
, Answer: it is a combination of forward selection and backward elimination.
We can either start with all factors or no factors and at each step we remove
or add a factor. As we go through the procedure after adding each new
factor and at the end we eliminate right away factors that no longer appear.
◍ Structured Data.
Answer: Quantitative, Categorical, Binary, Unrelated, Time Series
◍ what type of algorithms are stepwise selection?.
Answer: Greedy algorithms - at each step they take one thing that looks best
◍ what is LASSO.
Answer: a variable selection method where the coefficients are determined
by both minimizing the squared error and the sum of their absolute value not
being over a certain threshold t
◍ Unstructured Data.
Answer: Text
◍ How do you choose t in LASSO.
Answer: use the lasso approach with different values of t and see which
gives the best trade off
◍ Support Vector Model.
Answer: Supervised machine learning algorithm used for both classification
and regression challenges. Mostly used in classification problems by
plotting each data item as a point in n-dimensional space (n is the number of
features you have) with the value of each feature being the value of a
particular coordinate. Then you classify by finding a hyperplane that
differentiates the 2 classes very well. Support vectors are simply the
coordinates of individual observation -- it best segregates the two classes
(hyperplane / line).
◍ why do we have to scale the data for LASSO.
Answer: if we don't the measure of the data will artificially affect how big
the coefficients need to be
,◍ What do you want to find with a SVM model?.
Answer: Find values of a0, a1,...,up to am that classifies the points correctly
and has the maximum gap or margin between the parallel lines.
◍ What is elastic net?.
Answer: A variable selection method that works by minimizing the squared
error and constraining the combination of absolute values of coefficients and
their squares
◍ What should the sum of the green points in a SVM model be?.
Answer: The sum of green points should be greater than or equal to 1
◍ what is a key difference between stepwise regresson and lasso regression.
Answer: If the data is not scaled, the coefficients can have artificially
different orders of magnitude, which means they'll have unbalanced effects
on the lasso constraint.
◍ Why doesn't Ridge Regression perform variable selection?.
Answer: The coefficients values are squared so they go closer to zero or
regularizes them
◍ What should the sum of the red points in a SVM model be?.
Answer: The sum of red points should be less than or equal to -1
◍ What should the total sum of green and red points be?.
Answer: The total sum of all green and red points should be equal to or
greater than 1 because yj is 1 for green and -1 for red.
◍ First principal component.
Answer: PCA -- a linear combination of original predictor variables which
captures the maximum variance in the data set. It determines the direction of
highest variability in the data. Larger the variability captured in first
component, larger the information captured by component. No other
component can have variability higher than first principal component.it
minimizes the sum of squared distance between a data point and the line.
◍ What are the pros and cons of Greedy Algorithms (Forward selection,
, stepwise elimination, stepwise regression).
Answer: Good for initial analysis but often don't perform as well on other
data because they fit more to random effects than you'd like and appear to
have a better fit
◍ Second principal component.
Answer: PCA -- also a linear combination of original predictors which
captures the remaining variance in the data set and is uncorrelated with Z¹.
In other words, the correlation between first and second component should
is zero.
◍ What if it's not possible to separate green and red points in a SVM model?.
Answer: Utilize a soft classifier -- In a soft classification context, we might
add an extra multiplier for each type of error with a larger penalty, the less
we want to accept mis-classifying that type of point.
◍ Soft Classifier.
Answer: Account for errors in SVM classification. Trading off minimizing
errors we make and maximizing the margin.To trade off between them, we
pick a lambda value and minimize a combination of error and margin. As
lambda gets large, this term gets large.The importance of a large margin
outweighs avoiding mistakes and classifying known data points.
◍ What are the pros and cons of LASSO and elastic net.
Answer: They are slower but help make models that make better predictions
◍ Which two methods does elastic net look like it combines and what are the
downsides from it?.
Answer: Ridge Regression and LASSO.Advantages: variable selection from
LASSO and Predictive benefits of LASSO.Disadvantages: Arbitrarily rules
out some correlated variables like LASSO (don't know which one that is left
out should be); Underestimates coefficients of very predictive variables like
Ridge Regresison
◍ What are some downsides of surveys?.
Answer: Even if you what appears to be a representative sample in simple
FINAL TEST 2026 QUESTIONS WITH
CORRECT ANSWERS GRADED A+
◍ Rows.
Answer: Data points are values in data tables
◍ when might overfitting occur.
Answer: when the # of factors is close to or larger than the # of data points
causing the model to potentially fit too closely to random effects
◍ Why are simple models better than complex ones.
Answer: less data is required; less chance of insignificant factors and easier
to interpret
◍ Columns.
Answer: The 'answer' for each data point (response/outcome)
◍ what is forward selection.
Answer: we select the best new factor and see if it's good enough (R^2, AIC,
or p-value) add it to our model and fit the model with the current set of
factors. Then at the end we remove factors that are lower than a certain
threshold
◍ what is backward elimination.
Answer: we start with all factors and find the worst on a supplied threshold
(p = 0.15). If it is worse we remove it and start the process over. We do that
until we have the number of factors that we want and then we move the
factors lower than a second threshold (p = .05) and fit the model with all set
of factors
◍ what is stepwise regression.
, Answer: it is a combination of forward selection and backward elimination.
We can either start with all factors or no factors and at each step we remove
or add a factor. As we go through the procedure after adding each new
factor and at the end we eliminate right away factors that no longer appear.
◍ Structured Data.
Answer: Quantitative, Categorical, Binary, Unrelated, Time Series
◍ what type of algorithms are stepwise selection?.
Answer: Greedy algorithms - at each step they take one thing that looks best
◍ what is LASSO.
Answer: a variable selection method where the coefficients are determined
by both minimizing the squared error and the sum of their absolute value not
being over a certain threshold t
◍ Unstructured Data.
Answer: Text
◍ How do you choose t in LASSO.
Answer: use the lasso approach with different values of t and see which
gives the best trade off
◍ Support Vector Model.
Answer: Supervised machine learning algorithm used for both classification
and regression challenges. Mostly used in classification problems by
plotting each data item as a point in n-dimensional space (n is the number of
features you have) with the value of each feature being the value of a
particular coordinate. Then you classify by finding a hyperplane that
differentiates the 2 classes very well. Support vectors are simply the
coordinates of individual observation -- it best segregates the two classes
(hyperplane / line).
◍ why do we have to scale the data for LASSO.
Answer: if we don't the measure of the data will artificially affect how big
the coefficients need to be
,◍ What do you want to find with a SVM model?.
Answer: Find values of a0, a1,...,up to am that classifies the points correctly
and has the maximum gap or margin between the parallel lines.
◍ What is elastic net?.
Answer: A variable selection method that works by minimizing the squared
error and constraining the combination of absolute values of coefficients and
their squares
◍ What should the sum of the green points in a SVM model be?.
Answer: The sum of green points should be greater than or equal to 1
◍ what is a key difference between stepwise regresson and lasso regression.
Answer: If the data is not scaled, the coefficients can have artificially
different orders of magnitude, which means they'll have unbalanced effects
on the lasso constraint.
◍ Why doesn't Ridge Regression perform variable selection?.
Answer: The coefficients values are squared so they go closer to zero or
regularizes them
◍ What should the sum of the red points in a SVM model be?.
Answer: The sum of red points should be less than or equal to -1
◍ What should the total sum of green and red points be?.
Answer: The total sum of all green and red points should be equal to or
greater than 1 because yj is 1 for green and -1 for red.
◍ First principal component.
Answer: PCA -- a linear combination of original predictor variables which
captures the maximum variance in the data set. It determines the direction of
highest variability in the data. Larger the variability captured in first
component, larger the information captured by component. No other
component can have variability higher than first principal component.it
minimizes the sum of squared distance between a data point and the line.
◍ What are the pros and cons of Greedy Algorithms (Forward selection,
, stepwise elimination, stepwise regression).
Answer: Good for initial analysis but often don't perform as well on other
data because they fit more to random effects than you'd like and appear to
have a better fit
◍ Second principal component.
Answer: PCA -- also a linear combination of original predictors which
captures the remaining variance in the data set and is uncorrelated with Z¹.
In other words, the correlation between first and second component should
is zero.
◍ What if it's not possible to separate green and red points in a SVM model?.
Answer: Utilize a soft classifier -- In a soft classification context, we might
add an extra multiplier for each type of error with a larger penalty, the less
we want to accept mis-classifying that type of point.
◍ Soft Classifier.
Answer: Account for errors in SVM classification. Trading off minimizing
errors we make and maximizing the margin.To trade off between them, we
pick a lambda value and minimize a combination of error and margin. As
lambda gets large, this term gets large.The importance of a large margin
outweighs avoiding mistakes and classifying known data points.
◍ What are the pros and cons of LASSO and elastic net.
Answer: They are slower but help make models that make better predictions
◍ Which two methods does elastic net look like it combines and what are the
downsides from it?.
Answer: Ridge Regression and LASSO.Advantages: variable selection from
LASSO and Predictive benefits of LASSO.Disadvantages: Arbitrarily rules
out some correlated variables like LASSO (don't know which one that is left
out should be); Underestimates coefficients of very predictive variables like
Ridge Regresison
◍ What are some downsides of surveys?.
Answer: Even if you what appears to be a representative sample in simple
