redskunk221

redskunk221

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JesĆŗs

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Statistics38
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For this hypothesis we being defining the null hypothesis and alternative hypo...
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Researchers measured the blood pressures of 22 students in
two situations: when the students were relaxed and when the students were taking an
important examination. The file
systolic.txt
lists the systolic and diastolic pressures for
each student in each situation. Dataset is available at
systolic_url = "https://mcs.utm.utoronto.ca/~nosedal/data/systolic.txt"
Use R
to answer each of the following questions. You have to show
all your work
to get
full credit.
Answers, even if correct, with no justifications will not receive any
marks
.
a)
Using R
, compute the change in systolic pressure by subtracting systolic pressure when
relaxed from systolic pressure during the exam; call this variable
X
.
b)
Using R
, repeat part a) for diastolic pressure. Call the resulting variable
Y
.
c)
Using R
, make a scatterplot of
Y
versus
X
, fit a regression line to the data and add
this line to your plot. Use: 2 c) your last name as the main title for your plot. (In my
case it would be: 2 c) Nosedal).
Important note. Use
Y
as your response variable and label horizontal and vertical axes.
d) What is the fitted regression model? Provide slope and y-intercept
explicitly
.
Use R
to find them.
e)
Using R
, make a residual plot from the regression in part c). Does this plot call into
question the use of a linear model? Why?
Note. Use:
2 e) your last name
as the main title for your plot. (In my case it would
be:
2 e) Nosedal
).
f) Note the outlier in the residual plot (and on the scatterplot from part c)).
Using
R
, delete the outlier from the data set. Then repeat parts c) and e). Note. Use:
2 f )
your last name
as the main title for your plots. (In my case it would be:
2 f ) Nosedal
).
g) What is the fitted regression model (after the outlier has been removed)? Provide slope
and y-intercept
explicitly
.
Use R
to find them.
The plots and the graphs are attached, the code included in the file is this. ...
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First of all we calculate the correlation of these two variables, the formula ...

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