Chapter 5 quiz - 5-4 Quiz_ Python Functions
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School
Southern New Hampshire University *
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Course
243
Subject
Mathematics
Date
Jan 9, 2024
Type
docx
Pages
6
Uploaded by EarlSteel9150
Question
1
3/
3
points
The
scores
received
in
four
exams
in
a
math
class are
collected
for
50
students.
The
following
Python
code
is
used
to
fit
a
simple
linear
regression
model
using
data
from
the
ExamScores.csv
file.
Which
of
the
two
variables,
“Exam4”
or
“Exam2”,
is
the
response
variable?
Which
is
the
predictor
variable?
Select
one.
import
pandas
as
pd
import
statsmodels.formula.api
as
smf
scores
=
pd.read_csv('http:/data-analytics.zybooks.com/ExamScores.csv')
model
=
smf.ols('Exam4
~
Exam2!,
scores).fit()
Exam2
and
Exam4
are
both
response
variables.
o
Exam4
is
the
response
variable
and
Exam2
is
the
predictor
variable.
Exam2
and
Exam4
are
both
predictor
variables.
Exam2
is
the
response
variable
and
Exam4
is
the
predictor
variable.
Question
2
3/
3
points
Which
of
the
following
correctly
represents
the
coefficient
of
determination
in
terms
of
the
variance
that
is
an
output
from
the
analysis
of
variance
table? Select
one.
explained
variance
+
unexplained
variance
total
variance
1
total
variance
@)
explained
variance
total
variance
unexplained
variance
total
variance
Question
3
0/
3
points
The
ols()
method
in
statsmodels
is
used
to
fit
a
simple
linear
regression
model
using
“Exam4”
as
the
response
variable
and
“Exam3”
as
the
predictor
variable.
The
output
is
shown
below.
A
text
version
is
available.
What
is
the
correct
regression
equation
based
on
this
output?
Is
this
model
statistically
significant
at
10%
level
of
significance
(alpha
=
0.10)?
Select
one.
(Hint:
Review
results
of
F-statistic)
OLS
Regression
Results
R-squared:
Adj.
R-squared:
F-statistic
Prob
(F-statistic):
Log-Likelihood:
No.
Observations:
AIC:
Df
Residuals:
BIC:
DF
Model:
Covariance
Type:
0.077
0.058
4.010
0.0509
-172.76
349.5
353.3
P>lt]
[0.025
68.9586
17.568
0.000
61.066
0.975]
76.851
0.206
0.1028
(X5
2.002
0.051
-0.000
omnibus:
5.557
Durbin-Watson:
Prob(Omnibus)
:
0.062
Jarque-Bera
(
skew:
0.659
Prob(38):
Kurtosi
3.621
Cond.
No.
Exam4
=
68.9576
+
0.1028
Exam3,
model
is
not
statistically
significant
1.644
4.422
0.110
271.
Exam4
=
76.85
+
0.206
Exam3,
model
is
not
statistically
significant
Exam4
=
68.9576
+
0.1028
Exam3,
model
is
statistically
significant
o
Exam4
=
76.85
+
0.206
Exam3,
model
is
statistically
significant
Question
4
3/
3
points
Which
of
the
following
python
methods
can
be
used
to
perform
simple
linear
regression
on
a
data
set?
Select
all
that
apply.
|
linregress
method
from
scipy
module
simplelinearregression
from
scipy
module
|
ols
method
from
statsmodels
module
Question
5
3/
3
points
An
online
shopping
website
collected
data
regarding
its
operations
and
obtained
the
following
linear
regression
model
for
the
estimated
revenue
in
millions,
¥,
based
on
the
click-through
rate
in
thousands,
x.
?=12+02¢
What
is
the
best
interpretation
of
the
value
of
the
estimated
slope
of
0.2?
Select
one.
The
estimated
change
in
the
click-through
rate
is
0.2
thousand
for
each
for
each
one
million
in
revenue.
o
The
estimated
change
in
revenue
for
each
additional
thousand
clicks
is
$0.2
million.
When
there
are
no
clicks
on
the
website,
the
estimated
revenue
is
$0.2
million.
Every
click
on
the
website
causes
$0.2
million
more
revenue.
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Question
1
3/
3
points
The
linregress()
method
in
scipy
module
is
used
to
fit
a
simple
linear
regression
model
using
“Reaction”
(reaction
time)
as
the
response
variable
and
“Drinks”
as
the
predictor
variable.
The
output
is
shown
below.
What
is
the
correct
regression
equation
based
on
this
output?
Is
this
model
statistically
significant
at
5%
level
of
significance
(alpha
=
0.05)?
Select
one.
000000000000001,
intercej
SELELELEELELEITY
28166526882823,
pvalue=0.0010983582017795293,
stderr=0.7141428428542851)
Reaction
=
3.9999
+
6.0000
Drinks,
model
is
not
statistically
significant
Reaction
=
6.0000
+
3.9999
Drinks,
model
is
statistically
significant
Reaction
=
6.0000
+
3.9999
Drinks,
model
is
not
statistically
significant
'
Reaction
=
3.9999
+
6.0000
Drinks,
model
is
statistically
significant
Question
2
3/
3
points
Which
of
the
following
Python
methods
return
the
correlation
coefficient?
Select
all
that
apply.
|
pearsonr
method
from
scipy.stats
submodule
|
corr
method
from
pandas
dataframe
Question
3
0/3p
The
ols()
method
in
statsmodels
was
used
to
fit
a
simple
linear
regression
model
using
“Exam4”
as
the
response
variable
and
“Exam1”
as
the
predictor
variable.
The
output
is
shown
below.
A
text
version
is
available.
What
is
the
correct
regression
equation
based
on
this
output?
Is
this
model
statistically
significant
at
5%
level
of
significance
(alpha
=
0.05)? Select
one.
(Hint:
Review
results
of
F-statistic)
OLS
Regression
Results
Method:
Date:
Time:
No.
Observations:
DF
Residuals:
DF
Model:
Covariance
Type:
-173.00
350.0
EIEN
P>lt|
[0.025
Intercept
57.7627
10.052
5.746
0.000
37.552
Exanl
0.2266
0.121
1.876
0.067
0016
0.975]
77.973
[R1T]
omnibu:
3.859
Durbin-watson:
Prob(Omnibus’
0.145
Jarque:
a
(I8
Skew:
_
0.428
I‘::g(]fl
Examé
=77.973
+0.469
Examl,
model
is
not
statistically
significant
Examd
=
57.7627
+
0.2266
Exam1,
model
is
not
statistically
significant
Examd
=77.973
+0.469
Examl,
model
is
statistically
significant
®
)
Examd
=57.7627
+
0.2266
Exam1,
model
is
statistically
significant
Question
4
0/
3
points
Which
of
the
following
Python
methods
in
scipy.stats
submodule
returns
the
P-value
for
performing
a
hypothesis
test
for
the
significance
of
the
correlation
coefficient?
Select
one.
pearsonr
from
pandas
module
@
pearson
from
scipy
module
pearsonr
from
scipy
module
pearson
from
pandas
module
Question
5
3/
3
points
The
height
and
grade
point
average
of
50
randomly
selected
students
are
recorded.
The
following
Python
code
is
used
to
fit
a
simple
linear
regression
model
using
data
collected
from
the
sample
that
is
stored
in
the
gpa.csv
file.
Which
of
the
two
variables,
“gpa”
or
“height”,
is
the
response
variable?
Which
is
the
predictor
variable?
Select
one.
import
pandas
as
pd
import
statsmodels.formula.api
as
smf
scores
=
pd.read_csv('http:/data-analytics.zybooks.com/gpa.csv')
model
=
smf.ols(gpa
~
height!,
scores).fit()
height
is
the
response
variable
and gpa
is
the
predictor
variable.
»
)
gpa
is
the
response
variable
and
height
is
the
predictor
variable.
gpa and
height
are
both
response
variables.
gpa and
height
are
both
predictor
variables.
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