Ques 5 / 5 pts tion 1
In a linear regression problem, we are using “R-squared” to measure
goodness-of-fit. We add a feature (variable) in linear regression model
and retrain the same model.
Which of the following o
...
Ques 5 / 5 pts tion 1
In a linear regression problem, we are using “R-squared” to measure
goodness-of-fit. We add a feature (variable) in linear regression model
and retrain the same model.
Which of the following option is true?
If R Squared increases, this variable is significant.
If R Squared decreases, this variable is not significant.
Individually R squared cannot tell about variable importance. We can’t say
anything about it right now.
None of these.
Ques 0 / 5 pts tion 2
Incorrect
A correlation between age and health of a person found to be -1.09. On
the basis of this, you would tell the doctors that:
Age is a good predictor of health
Health is a good predictor of age
Age is a poor predictor of health
None of these
Ques 5 / 5 pts tion 3
You work for a bank where you are trying to predict the probability of
default of a customer based on FICO score and annual income. Which of
4/14/2020 Graded Homework #2: Data Analytics Business - MGT-6203-OAN
https://gatech.instructure.com/courses/94184/quizzes/99397?module_item_id=558150 3/13
the following problems can arise while using a multiple linear regression
model?
There exists homoskedasticity in the model.
The model can produce predicted probabilities that are less than zero and
greater than one.
The model leads to the omitted variable bias as only two independent
factors can be included in the model.
The model leads to an overestimation of the effect of independent
variables on the dependent variable.
We try to build a model for NBA players’ salary.
Download the dataset nba2017.csv from here:
https://www.dropbox.com/s/pe3urv1mv9s8mwb/nba2017%20%281%29.csv
(https://www.dropbox.com/s/pe3urv1mv9s8mwb/nba2017%20%281%29.csv)
(https://gatech.box.com/s/qdkpwlxxo0tyxs4kw0m8wyxec5fbhvc7)
Load the dataset using the code nba = read.csv("nba2017.csv", header =
TRUE).
Now we take a closer look at the data set. There are four variables salary,
Ht(Height), Exp(Experience) and expsq(the square of Experience).
First, build a model using salary as the response and Ht and Exp as
variables and denote it as Model_1. Build a second model using
log(salary) as the response and Ht and Exp as variables, we denote it as
Model_2.
4/14/2020 Graded Homework #2: Data Analytics Business - MGT-6203-OAN
https://gatech.instructure.com/courses/94184/quizzes/99397?module_item_id=558150 4/13
Ques 5 / 5 pts tion 4
For Model_1, what is the interpretation for the coefficient of height?
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