The goal of this assignment is to analyze one or two quantitative variables (that may be related to each other) to see i

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The goal of this assignment is to analyze one or two
quantitative variables (that may be related to each other) to see
if you can predict one from the other. The data set should consist
of a set of individual people or things (30 or more) on which
variable(s) have been measured (one to two). Variables need to be
continuous, unless testing proportions. The goal is to analyze the
data and to present the results so that someone who has not had a
statistics course could understand them. For example, when you
report the p-value of a hypothesis test, you need to explain what
it means and why you would probably reject (or fail to reject) the
null hypothesis. You don't need to explain how the tests of
hypotheses work, but you do need to explain what the assumptions
are and the decision means. The project will have to address five
main topics: 1) What question are you trying to answer? (e.g., Can
the height of students be used to predict how far someone can
jump?) 2) Why is this question of interest? (e.g., In grade school
one of the tests in gym class is to see how far you can jump. Is
this fair to people who are short?) 3) How was the data gathered,
and what limitations does this imply? How would you overcome these
limitations? (e.g., Only students in the fifth period gym class
were used, this is bad because…) 4) Describe the relationship
between the variables. (e.g., The jumping distance is predicted to
increase by … for each additional inch of height…) 5) Verify that
your model is appropriate (or take remedial action if needed) by
checking the assumptions of the model (e.g., Based on this plot,
the error variance appeared non-constant, so I …) The paper should
be typed, using complete sentences, good grammar, and transition
between the various sections. If you are using data collected by
someone else or a website, reference the source appropriately.
Specifics for the Project 1) If the data come from a sample: Define
the desired target population and describe how the sample was
collected. If you were not able to sample from the desired
population, state what differences you might expect between the
population that was actually sampled from and the desired target
population. If you were not able to take a simple random sample
from the population, discuss how the sampling could be improved if
you were allowed more money and time. If the data come from an
experiment: Describe how the experiment was carried out, describe
any sources of extra variation (e.g. changing temperature,
different people making the measurements, etc...). Did you try to
control these? Discuss how the experiment could be improved if you
were allowed (more) money and time. 2) Statistics: The report of
the test you performed should include at least the following
statistics: a confidence interval, the p-value for testing, and
descriptive statistics & graphs. Make sure and tell the reader
why these statistics should be useful to them, and interpret them
in the context of your data set. Assumptions: Check the assumptions
needed. Give relevant plots that you use to check the model
assumptions (see above). 3) Finally, don’t forget to include a
short summary at the end of your paper to tie everything
together!