PSY420M - PolyFits Lab
pdf
keyboard_arrow_up
School
University of Texas *
*We aren’t endorsed by this school
Course
420M
Subject
Industrial Engineering
Date
Dec 6, 2023
Type
Pages
6
Uploaded by CountRiver6830
Polynomial Fits Lab Report
Tyler Peterson
17.11.2023
Shape vs Hours dataset
1)
Plot of dependent variable vs hrs/day working out with a 1st order polynomial
(straight line fit)
Based on this plot, the more hours people work out, the more in shape they tend to be/
feel.
2)
Plot of “leftover” residuals from the straight-line fit
Based on this residual plot, it is clear that the line of best fit from the original straight line
plot represents the data correctly, and the residual accounts for the true meaning of the
data.
BPM vs Hours dataset
3)
Plot of dependent variable vs hrs/day working out with a 1st order polynomial
(straight line fit)
Based on this graph, the straight line fit does not accurately represent the true meaning of
the data since the points do not correctly line up with the straight line.
4)
Plot of “leftover” residuals from the straight-line fit
Based on this residual plot, the straight-line fit plot does not accurately represent the true
meaning of the data due to where the points are (the spread) on the graph.
5)
Plot of dependent variable vs hrs/day with a 2nd order polynomial (curvy line) fit
Based on this plot, the curvy line fit is a better representation of the data set. This curvy
line lines up more with the given data showing a better visual trend of such. This means
that the more hours people work out, over time, their heart rate begins to decrease slowly.
6)
Plot of residuals “leftover” from the curvy fit
Based on this residual plot from the curvy fit, the curvy fit line better represents this data
set. The plots on this graph are less spaced out showing the true difference between the
data points from this data.
Your preview ends here
Eager to read complete document? Join bartleby learn and gain access to the full version
- Access to all documents
- Unlimited textbook solutions
- 24/7 expert homework help
Productivity vs Hours dataset
7)
Plot of dependent variable vs hrs/day working out with a 1st order polynomial
(straight line fit)
This straight-line plot is not a good representation of the data because almost none of the
points align with the straight line.
8)
Plot of residuals “leftover” from the straight line fit
This residual plot gives evidence for the claim of the straight-line plot not being a good
representation of this data set. From this residual plot, we can see that there is no real
trend between the data with the straight line plot and that this type of plot does not show a
true correlation in the data set.
9)
Plot of dependent variable vs hrs/day with a 2nd order polynomial (curvy line) fit
This curvy line fit is once again a much better representation of our data set. Here, we can
see that the curved line better lines up with the data points.
10) Plot of residuals left over from the curvy fit
Once again, based on this residual plot data we can see that the differences (spread) shown in
these points are a much better representation of our data set compared to the residuals from the
straight line fit.
Conclusion:
In conclusion, only one of these data sets was best represented by a straight-line fit. Only the first
dataset comparing how in shape people are compared to how much they work out was best
represented by a straight line. That data set has a linear positive correlation; the more people
work out per day, the more in shape they tend to be. For the bpm vs hours per day working out,
the straight line fit was not a good representation of the data. After conducting a curvy line fit I
found that that better matched the data. The residual from the curvy line fit also proved that the
data set was much better expressed when a curvy line fit was used. The more hours people
worked out, their heart rate would decrease but at an exponential/ slow rate. For the last data set,
productivity and hours worked out per day, the data set was also best represented by a curvy line
fit. The straight line fit for this graph showed the worst correlation for the data set, with almost
none of the points falling on the line. However, the curvy line fit better aligned with the data
points, and the residual for that plot also gave a better picture of how the points in the data were
correlated. This data set’s graph was a negative parabola. This means that when people worked
out anywhere between 1 hour to about 2.5 hours, their productivity increased, but any more
hours of working out after that, their productivity began to slowly decrease. Overall, residuals
can help us determine if the type of data plot we did (straight line fit or curvy fit) is an accurate
representation of our data.
Your preview ends here
Eager to read complete document? Join bartleby learn and gain access to the full version
- Access to all documents
- Unlimited textbook solutions
- 24/7 expert homework help