Math 220 _ Project 2
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School
James Madison University *
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Course
220
Subject
Health Science
Date
Feb 20, 2024
Type
Pages
2
Uploaded by mahek92811
Project 2
Blood pressure: A blood pressure measurement consists of two numbers: the systolic pressure,
which is the maximum pressure taken when the heart is contracting, and the diastolic pressure,
which is the minimum pressure taken at the beginning of the heartbeat. Blood pressures were
measured, in millimeters, for a sample of 16 adults. The following table presents the results.
Based on results published in the
Journal of Human Hypertension
1. Use SPSS to summarize the diastolic pressure
.
2. Describe the distribution of diastolic pressure.
Shape:
Skewed to the right
Outliers
: none
Mean:
79.3750
Minimum
: 66.00
Q1
: 70.2500
Median
: 76.5000
Q3
: 87.7500
Maximum
: 103.00
Standard Deviation
: 10.5696
1
3. Plot Diastolic vs. Systolic using a scatter plot and describe the relationship
.
4. Find Pearson correlation coefficient and interpret it
.
R= 0.8568, this number indicates a high positive correlation which means the high x (systolic)
values go with high y (Diastolic) values and low x values go with low y values.
5. Compute the least-squares regression line for predicting the diastolic pressure from the
systolic pressure
.
ŷ=9.1828+0.5748x
6. Is it possible to interpret the y-intercept? Explain
.
No, because all of the variables are positive, therefore it is not possible to interpret the
y-intercept.
7. If the systolic pressures of two patients differ by 10 millimeters, by how much would you
predict their diastolic pressures to differ?
If the systolic pressure of two patients differs by 10 millimeters we would predict it to be
differentiated by 5.748.
8. Predict the diastolic pressure for a patient whose systolic pressure is 130 millimeters
.
The diastolic pressure for a patient whose systolic pressure is 130 millimeters we predict it to be
83.9068.
9. Find the residual for the predicted value in (8)
.
The residual for the predicted value for 8 would be 7.9068.
10. Is the line a good fit? Why or why not?
The line is a good fit because r^2 is higher than 0.5 indicating that most data falls near the line.
Also, by looking at the scatterplot you can see that each point is relatively near the line.
2
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