Badm 211 Exam 2 – Questions With Finalized
Solutions
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Terms in this set (16)
Recall that for each b
record, we
mathematically define
error as 𝑒 = 𝑦 − 𝑦&,where
𝑦 is the actual outcome
and 𝑦& is the predicted
outcome.What does a
negative error for a
record imply?
A. We are
underestimating the
outcome for that record.
B. We are overestimating
the outcome for that
record.
C. We are perfectly
predicting the outcome
for that record.
D. Mean Absolute Error
(MAE) is negative
Multiple linear regression A
is an example of which of
the following?
A. Regression
B. Classification
, Which of the following is A
a regression problem:1.
Predicting the price at
which an item will be
sold2. Predicting whether
an item will be soldSelect
all that apply.
A. Only 1
B. Only 2
C. Both 1 and 2
D. Neither 1 nor 2
If your predictive model A
is too complex, you will
be more likely to overfit
the training data.
A. True
B. False
Why do we drop a C
variable when dummy
coding?
A. To make the data
easier to work with
B. To make a predictive
model less complex
C. To avoid
multicollinearity`
D. To avoid overfitting
When assessing B
predictive accuracy for
regression models, a
drawback of using RMSE
is that positive errors can
cancel out negative
errors.
A. True
B. False
Solutions
Save
Terms in this set (16)
Recall that for each b
record, we
mathematically define
error as 𝑒 = 𝑦 − 𝑦&,where
𝑦 is the actual outcome
and 𝑦& is the predicted
outcome.What does a
negative error for a
record imply?
A. We are
underestimating the
outcome for that record.
B. We are overestimating
the outcome for that
record.
C. We are perfectly
predicting the outcome
for that record.
D. Mean Absolute Error
(MAE) is negative
Multiple linear regression A
is an example of which of
the following?
A. Regression
B. Classification
, Which of the following is A
a regression problem:1.
Predicting the price at
which an item will be
sold2. Predicting whether
an item will be soldSelect
all that apply.
A. Only 1
B. Only 2
C. Both 1 and 2
D. Neither 1 nor 2
If your predictive model A
is too complex, you will
be more likely to overfit
the training data.
A. True
B. False
Why do we drop a C
variable when dummy
coding?
A. To make the data
easier to work with
B. To make a predictive
model less complex
C. To avoid
multicollinearity`
D. To avoid overfitting
When assessing B
predictive accuracy for
regression models, a
drawback of using RMSE
is that positive errors can
cancel out negative
errors.
A. True
B. False