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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 use 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 obtaining your data. The
requirements for this project are discussed in detail below.
A. Suggested Paragraph Structure for the Written Report (Minimum 2 pages, double-spaced)
Paragraph 1: First, give an introduction discussing the problem being studied, some background
on the topic, and why it is of interest to you. Second, mention how your data was obtained,
citing your data source. Lastly, describe which variable would be the explanatory variable and
which one is the response variable, and why so.
Paragraph 2: First, interpret the meaning of the correlation coefficient from your summary
output in in a sentence for your project example (follow the structure that I taught in my notes
and videos). Second, include the linear regression equation in the report with the determined
intercept and slope values. Third, interpret the slope and intercept values from the regression
equation in sentences (follow the structure that I taught in my notes and videos).
Paragraph 3: First, state what the coefficient of determination value is, and interpret its
meaning in a sentence for your project (follow the structure that I taught in my notes and
videos). Second, perform diagnostics on the regression model using the residual plots from your
summary, based on material taught in section 4.3 (chapter 4); here comment on (1) whether or
not a linear model should be appropriate, (2) whether or not the residual error term appears to
have constant variance, and (3) whether or not there are any outliers.
Paragraph 4: Briefly describe, in conclusion, if this regression model does a good job in
explaining the dataset, based on your Excel findings. Here you can make additional comments
about the regression model that you think are worth mentioning. This is your chance to be
creative and provide additional insight, but there is not really a right or wrong way to do this.
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 (if you worked
in a group). [If you worked alone then you will not need to do anything for this part e)].
Note: Full credit (100%) will be given on the project if you: (1) follow correctly all of the
guidelines from the suggested framework (from this document) for the report section with
proper sentence interpretations for your project variables, and if your report is well-written ,
(2) if you follow the requirements for the appendix graphs and charts shown right above on this
page. Thus, if you allow enough time for this project and follow my suggestions it should not be
hard to get an ‘A’ grade on it.
Each group only has to turn in 1 paper (including Excel graphs or calculations. This will be
submitted on Canvas. I will check the report for plagiarism.
*PLEASE PROVIDE TURNITIN REPORT*