ISYE 6501 MIDTERM QUESTIONS WITH
ANSWERS VERIFIED BY EXPERTS
2024 NEW UPDATED
What measure is used to determine the quality of a linear regression line to data? -
CORRECT ANSWER>>>>Square of the difference between the line and the data
points. (Sum squared error.)
What is the formula for point error in linear regression? - CORRECT ANSWER>>>>y(i)-
Yhat(i)=y(i)-(a(0) + a(i)*x(i)
Taking partial derivates and setting them equal to zero and then solving that system
equation helps us do what? - CORRECT ANSWER>>>>Minimize the error and optimize
the coefficients for linear regression.
What does AIC stand for? - CORRECT ANSWER>>>>Akaike Information Criterion
What is "Likelihood"? - CORRECT ANSWER>>>>A measure for the probability density
for any parameter set.
What is Maximum likelihood? - CORRECT ANSWER>>>>Parameters that give the
highest probability.
What is MLE? - CORRECT ANSWER>>>>Maximum Likelihood Estimate... The set of
parameters that minimizes the sum of square errors.
What is the formula for AIC? - CORRECT ANSWER>>>>AIC=2k - 2*ln(L*) where L* is
the maximum likelihood value and K is the number of parameters estimated.
What is the penality term in AIC and what does it do? - CORRECT ANSWER>>>>2K -
It helps prevent overfitting.
A models that is fit to random effects and not real ones is said to be? - CORRECT
ANSWER>>>>Overfit
What does corrected AIC account for? - CORRECT ANSWER>>>>The fact that we
cannot have infinitely many data points.
,If AIC for model 1 is 75 and AIC for model 2 is 80 how do you compute which is better. -
CORRECT ANSWER>>>>It is the relative likelyhood: e^((AIC1-AIC2)/2) = 8.2%
What does BIC stand for? - CORRECT ANSWER>>>>Bayesian Information Criterion
If you have a lot more data than parameters should you use AIC or BIC? - CORRECT
ANSWER>>>>BIC
If you have "fewer" parameters - CORRECT ANSWER>>>>should you use AIC or
BIC?, BIC
The "Rule of Thumb" deals with? - CORRECT ANSWER>>>>When comparing two
models on the same data set by their BIC scores.
In exponential smoothing what does alpha do? - CORRECT ANSWER>>>>Adjust the
trade off between current (x_t) and previous values.
In exponential how should you adjust for randomness? - CORRECT
ANSWER>>>>Make alpha close to 0.
In exponential trending, what does Beta do? - CORRECT ANSWER>>>>Adjusts for
trending.
In exponential smoothing, what is C_t? - CORRECT ANSWER>>>>A multiplicative
seasonality factor at time t.
In exponential smoothing, what is L? - CORRECT ANSWER>>>>The length of a cycle.
In exponential smoothing, what does gamma do? - CORRECT ANSWER>>>>Adjusts
how much cycles contribute to the model.
In multiplicative seasonality, the first L values of C are set to what? - CORRECT
ANSWER>>>>1.
1-norm - CORRECT ANSWER>>>>Similar to rectilinear distance; measures the sum of
the lengths of each dimension
2-norm - CORRECT ANSWER>>>>Similar to Euclidian distance; measures the
straight-line length of a vector from the origin.
Additive seasonality - CORRECT ANSWER>>>>Seasonal effect that is added to a
baseline value.
, Adjusted R-squared/Adjusted R2 - CORRECT ANSWER>>>>Variant of R2 that
encourages simpler models by penalizing the use oftoo many variables.
AIC - CORRECT ANSWER>>>>Akaike information criterion
Akaike information criterion - CORRECT ANSWER>>>>Model selection technique that
trades off between model fit and model complexity. Model with lower AIC is preferred.
Generally penalizes complexity less than BIC.
Algorithm - CORRECT ANSWER>>>>Step-by-step procedure designed to carry out a
task.
Analysis of Variance/ANOVA - CORRECT ANSWER>>>>Statistical method for dividing
the variation in observations among different sources.
Area under curve/AUC - CORRECT ANSWER>>>>Area under the ROC curve; an
estimate of the classification model's accuracy. Also called concordance index.
ARIMA - CORRECT ANSWER>>>>Autoregressive integrated moving average.
Attribute - CORRECT ANSWER>>>>A characteristic or measurement - for example, a
person's height or the color of a car. Aka "feature", "covariate" or "predictor"
Autoregression - CORRECT ANSWER>>>>Regression technique using past values of
time series data as predictors of future values.
Autoregressive integrated moving average (ARIMA) - CORRECT ANSWER>>>>Time
series model that uses differences between observations when data is nonstationary.
Also called Box-Jenkins.
Bayes' theorem/Bayes' rule - CORRECT ANSWER>>>>Fundamental rule of conditional
probability: 𝑃(𝐴|𝐵) = 𝑃(𝐵|𝐴)𝑃(𝐴) / 𝑃(𝐵) .
Bayesian Information criterion - CORRECT ANSWER>>>>(BIC) Model selection
technique that trades off model fit and model complexity. Generally penalizes
complexity more than AIC. Lower is better.
Bayesian regression - CORRECT ANSWER>>>>Regression model that incorporates
estimates of how coefficients and error are distributed.
BIC - CORRECT ANSWER>>>>Bayesian information criterion
ANSWERS VERIFIED BY EXPERTS
2024 NEW UPDATED
What measure is used to determine the quality of a linear regression line to data? -
CORRECT ANSWER>>>>Square of the difference between the line and the data
points. (Sum squared error.)
What is the formula for point error in linear regression? - CORRECT ANSWER>>>>y(i)-
Yhat(i)=y(i)-(a(0) + a(i)*x(i)
Taking partial derivates and setting them equal to zero and then solving that system
equation helps us do what? - CORRECT ANSWER>>>>Minimize the error and optimize
the coefficients for linear regression.
What does AIC stand for? - CORRECT ANSWER>>>>Akaike Information Criterion
What is "Likelihood"? - CORRECT ANSWER>>>>A measure for the probability density
for any parameter set.
What is Maximum likelihood? - CORRECT ANSWER>>>>Parameters that give the
highest probability.
What is MLE? - CORRECT ANSWER>>>>Maximum Likelihood Estimate... The set of
parameters that minimizes the sum of square errors.
What is the formula for AIC? - CORRECT ANSWER>>>>AIC=2k - 2*ln(L*) where L* is
the maximum likelihood value and K is the number of parameters estimated.
What is the penality term in AIC and what does it do? - CORRECT ANSWER>>>>2K -
It helps prevent overfitting.
A models that is fit to random effects and not real ones is said to be? - CORRECT
ANSWER>>>>Overfit
What does corrected AIC account for? - CORRECT ANSWER>>>>The fact that we
cannot have infinitely many data points.
,If AIC for model 1 is 75 and AIC for model 2 is 80 how do you compute which is better. -
CORRECT ANSWER>>>>It is the relative likelyhood: e^((AIC1-AIC2)/2) = 8.2%
What does BIC stand for? - CORRECT ANSWER>>>>Bayesian Information Criterion
If you have a lot more data than parameters should you use AIC or BIC? - CORRECT
ANSWER>>>>BIC
If you have "fewer" parameters - CORRECT ANSWER>>>>should you use AIC or
BIC?, BIC
The "Rule of Thumb" deals with? - CORRECT ANSWER>>>>When comparing two
models on the same data set by their BIC scores.
In exponential smoothing what does alpha do? - CORRECT ANSWER>>>>Adjust the
trade off between current (x_t) and previous values.
In exponential how should you adjust for randomness? - CORRECT
ANSWER>>>>Make alpha close to 0.
In exponential trending, what does Beta do? - CORRECT ANSWER>>>>Adjusts for
trending.
In exponential smoothing, what is C_t? - CORRECT ANSWER>>>>A multiplicative
seasonality factor at time t.
In exponential smoothing, what is L? - CORRECT ANSWER>>>>The length of a cycle.
In exponential smoothing, what does gamma do? - CORRECT ANSWER>>>>Adjusts
how much cycles contribute to the model.
In multiplicative seasonality, the first L values of C are set to what? - CORRECT
ANSWER>>>>1.
1-norm - CORRECT ANSWER>>>>Similar to rectilinear distance; measures the sum of
the lengths of each dimension
2-norm - CORRECT ANSWER>>>>Similar to Euclidian distance; measures the
straight-line length of a vector from the origin.
Additive seasonality - CORRECT ANSWER>>>>Seasonal effect that is added to a
baseline value.
, Adjusted R-squared/Adjusted R2 - CORRECT ANSWER>>>>Variant of R2 that
encourages simpler models by penalizing the use oftoo many variables.
AIC - CORRECT ANSWER>>>>Akaike information criterion
Akaike information criterion - CORRECT ANSWER>>>>Model selection technique that
trades off between model fit and model complexity. Model with lower AIC is preferred.
Generally penalizes complexity less than BIC.
Algorithm - CORRECT ANSWER>>>>Step-by-step procedure designed to carry out a
task.
Analysis of Variance/ANOVA - CORRECT ANSWER>>>>Statistical method for dividing
the variation in observations among different sources.
Area under curve/AUC - CORRECT ANSWER>>>>Area under the ROC curve; an
estimate of the classification model's accuracy. Also called concordance index.
ARIMA - CORRECT ANSWER>>>>Autoregressive integrated moving average.
Attribute - CORRECT ANSWER>>>>A characteristic or measurement - for example, a
person's height or the color of a car. Aka "feature", "covariate" or "predictor"
Autoregression - CORRECT ANSWER>>>>Regression technique using past values of
time series data as predictors of future values.
Autoregressive integrated moving average (ARIMA) - CORRECT ANSWER>>>>Time
series model that uses differences between observations when data is nonstationary.
Also called Box-Jenkins.
Bayes' theorem/Bayes' rule - CORRECT ANSWER>>>>Fundamental rule of conditional
probability: 𝑃(𝐴|𝐵) = 𝑃(𝐵|𝐴)𝑃(𝐴) / 𝑃(𝐵) .
Bayesian Information criterion - CORRECT ANSWER>>>>(BIC) Model selection
technique that trades off model fit and model complexity. Generally penalizes
complexity more than AIC. Lower is better.
Bayesian regression - CORRECT ANSWER>>>>Regression model that incorporates
estimates of how coefficients and error are distributed.
BIC - CORRECT ANSWER>>>>Bayesian information criterion
