BRM 3 - The Big Exam (version 2.5) New e st Ve rsion
w ith C omp lete Que stion s & A ccu rate
Deta iled A n swers\ Verifie d 100% \G raded
A +\Brand New!! Late st Upd ate 2026 -2027
The coefficient for mpg is -294. But what does this mean??
The model predicts that if mpg increases by 1, that the price decreases by
-294
The coefficient for _cons is 11905 or each variable. But what does this
mean??
We could consider this the starting point of our model. If all else is zero,
the price will be 11905 euros
,We have an R-squared of 0.2838. But what does this mean??
That our model can account for 28% of the variability in the data. It's not
that good of a model
Our model is:
reg price mpg foreign
How do we add a categorical variable called rep78? rep78 is the repair
record
reg price mpg foreign i.rep78
Imagine that for this model we want to check whether our assumptions
are satisfied. What command would we use?
reg price mpg foreign i.rep78
regcheck
,How should we interpret the output of regcheck?
6 regression assumption tests are done. For these tests, the output states
whether the model passes these tests. This output could be used to adapt
the model to make it fit our assumptions.
What is the plan of attack for transforming variables? (4 steps)
1. Should I really transform my variables? (nlcheck)
2. Partition x into dummies
3. Use ladder
4. Let Stata search for you (fp, mfp)
What do we do in the first "Should I really transform my variables?
(nlcheck)" step? (4 points)
- We check whether it is necessary to transform our variables.
- Is the linearity assumption met?
- Check this using nlcheck mpg
- If nlcheck p-value < 0.05 -> nlcheck mpg, noisily
, Why would we execute the following
code? reg price mpg foreign
nlcheck mpg
To check the linearity of the effect of mpg. If the p-value is less than 0.05,
this suggests that we should reject H0: "Adding dummy variables does not
help"
If our nlcheck p-value is less than 0.05 and we choose to reject "Adding
dummy variables does not help", then what should we do?
We can execute:
nlcheck mpg, noisily
To divide the continuous mpg in categorical subcategories. The output will
show which bins are, in addition of mpg, statistically significant predictors
to have in our model. By default, 10 bins are created
w ith C omp lete Que stion s & A ccu rate
Deta iled A n swers\ Verifie d 100% \G raded
A +\Brand New!! Late st Upd ate 2026 -2027
The coefficient for mpg is -294. But what does this mean??
The model predicts that if mpg increases by 1, that the price decreases by
-294
The coefficient for _cons is 11905 or each variable. But what does this
mean??
We could consider this the starting point of our model. If all else is zero,
the price will be 11905 euros
,We have an R-squared of 0.2838. But what does this mean??
That our model can account for 28% of the variability in the data. It's not
that good of a model
Our model is:
reg price mpg foreign
How do we add a categorical variable called rep78? rep78 is the repair
record
reg price mpg foreign i.rep78
Imagine that for this model we want to check whether our assumptions
are satisfied. What command would we use?
reg price mpg foreign i.rep78
regcheck
,How should we interpret the output of regcheck?
6 regression assumption tests are done. For these tests, the output states
whether the model passes these tests. This output could be used to adapt
the model to make it fit our assumptions.
What is the plan of attack for transforming variables? (4 steps)
1. Should I really transform my variables? (nlcheck)
2. Partition x into dummies
3. Use ladder
4. Let Stata search for you (fp, mfp)
What do we do in the first "Should I really transform my variables?
(nlcheck)" step? (4 points)
- We check whether it is necessary to transform our variables.
- Is the linearity assumption met?
- Check this using nlcheck mpg
- If nlcheck p-value < 0.05 -> nlcheck mpg, noisily
, Why would we execute the following
code? reg price mpg foreign
nlcheck mpg
To check the linearity of the effect of mpg. If the p-value is less than 0.05,
this suggests that we should reject H0: "Adding dummy variables does not
help"
If our nlcheck p-value is less than 0.05 and we choose to reject "Adding
dummy variables does not help", then what should we do?
We can execute:
nlcheck mpg, noisily
To divide the continuous mpg in categorical subcategories. The output will
show which bins are, in addition of mpg, statistically significant predictors
to have in our model. By default, 10 bins are created
