BADM 211 Fall 2024 Test 2 Practice Questions with Correct Answers
Question 1:
Classification
Answer:
A supervised learning problem when the outcome is numeric.
Question 2:
Regression
Answer:
A problem studied in this course.
Question 3:
Association Rules
Answer:
A problem studied in this course.
Question 4:
Unsupervised Learning
Answer:
A problem studied in this course.
Question 5:
Predictive model
Answer:
The more suitable model to evaluate whether an independent variable is significantly related to
the outcome variable.
Question 6:
Explanatory model
Answer:
The more suitable model to evaluate whether an independent variable is significantly related to
, the outcome variable.
Question 7:
Error
Answer:
Mathematically defined as = −.., where is the actual outcome and is the predicted outcome.
Question 8:
Negative error
Answer:
Implies we are underestimating the outcome for that record.
Question 9:
Multiple linear regression
Answer:
An example of regression.
Question 10:
R-squared
Answer:
A statistic used to evaluate the explanatory power of a model.
Question 11:
Underfitting
Answer:
Indicated by the prediction error in the training data being equal to that in test data.
Question 12:
Efficiency model
Answer:
Predicts efficiency based on two predictors: training and distraction.
Question 1:
Classification
Answer:
A supervised learning problem when the outcome is numeric.
Question 2:
Regression
Answer:
A problem studied in this course.
Question 3:
Association Rules
Answer:
A problem studied in this course.
Question 4:
Unsupervised Learning
Answer:
A problem studied in this course.
Question 5:
Predictive model
Answer:
The more suitable model to evaluate whether an independent variable is significantly related to
the outcome variable.
Question 6:
Explanatory model
Answer:
The more suitable model to evaluate whether an independent variable is significantly related to
, the outcome variable.
Question 7:
Error
Answer:
Mathematically defined as = −.., where is the actual outcome and is the predicted outcome.
Question 8:
Negative error
Answer:
Implies we are underestimating the outcome for that record.
Question 9:
Multiple linear regression
Answer:
An example of regression.
Question 10:
R-squared
Answer:
A statistic used to evaluate the explanatory power of a model.
Question 11:
Underfitting
Answer:
Indicated by the prediction error in the training data being equal to that in test data.
Question 12:
Efficiency model
Answer:
Predicts efficiency based on two predictors: training and distraction.