This small project will give you a chance to apply what you have learned about s

This small project will give you a chance to apply what you have learned about simple linear
regression and study a problem that you find interesting. In this project, you will perform a
statistical analysis to investigate how two quantitative variables (not qualitative variables) are
associated and how one influences the other. You can choose what two variables you are
interested in studying and will collect your own data in order to perform an analysis. I will
provide you with a few good sources from which you might be interested in collecting your
data. The requirements for this project are discussed in detail below.
You may work individually or with up to two other classmates maximum. Use of Excel or
other computer statistical software will be required to carry out various calculations, produce
tables and graphs, and to perform a statistical analysis. You will be required to write a 2.5 to 3
page report (double-spaced) briefly discussing the problem being studied, your analysis and
findings, as well as additional or concluding remarks. Note that the 2 to 3 page minimum length
does not include any tables or graphs (which will be included separately after the paper). You
will include the Excel graphs or charts produced after the report, as well as the raw data
collected. If you work with two other classmates I will expect a 3 page report as opposed to a
2.5 page report minimum. There will also be a presentation part of the project as well
(discussed in more detail below).
This project will be worth 100 points in total, but this project as a whole will count for 6% of
your overall course grade. I expect the report section of the project to be well-written with
good sentence structure and proper grammar. You must include an introduction and a strong
conclusion, as outlined in the requirements below:
A. Requirements for the Written Report (Minimum 2.5-3 pages, double-spaced)
a) An introduction paragraph discussing the problem being studied, some background on
the topic, and why it is of interest to you. Mention how your data was obtained and cite your
source.
b) Describe somewhere which variable would be the explanatory variable and which one is
the response variable, and why so.
c) Interpret the meaning of the correlation coefficient in context of the problem and what
this means.
d) Include the linear regression equation in the report with the determined intercept and
slope values. Also, interpret the slope and intercept values. Lastly, for a particular chosen value
of the explanatory variable predict what the response variable would be and interpret what this would mean in a sentence. (Make sure the explanatory variable value
chosen would make sense for this problem)
e) Find what the coefficient of determination value is, and interpret it in the context of this
problem being studied. Does this indicate that the model is a good fit for the data and why?
f) Discuss, based on your findings, if there is a significant linear relationship between the
two variables.
g) Perform diagnostics on the regression model using residual plots. Using these, comment
on whether or not a linear model should be appropriate, on whether or not the residual error
term appears to have constant variance, and on whether or not there are any outliers.
h) Briefly describe, in conclusion, if this regression model does a good job in explaining the
dataset, based on your Excel findings. Here you can also make additional comments about the
regression model that you think are worth mentioning. This is your chance to be creative and
provide additional insight.
B. Requirements for the Appendix After the Written Report:
a) A scatter diagram showing the relationship between the 2 variables being analyzed.
Include a graph of the linear regression equation in this plot as well. Be sure to label the axes
and the plot.
b) Show the tables, determined using Excel toolbars and functions, which display coefficient
values, t values for the regression coefficients, and the p-values.
c) The residual plot, as shown in class. Be sure to label the axes and the plot.
d) The raw data collected.
e) Describe how you and your group member each contributed to the project.