Math 075 PowerPoint/Keynote/Prezi Project Mod 3&4 Instructions Part II
Module 3 involves linear regression and Module 4 involves non-linear regression, so they
fit nicely together as one project.
Create a PowerPoint/Keynote/Prezi presentation of your topic that has been approved
by Prof. Grigoryan. Your presentation should be colorful and include your graphs,
statistics and important or interesting highlights. You will be presenting to the class.
1. Introduction: You are to introduce your topic with research information including:
a. A title slide with your project title, class, semester, year and instructor.
b. Background research information on your topic.
c. The variables you are analyzing.
d. Provide a scatterplot of your data (without a regression curve)
e. What models you will be using to model your data.
2. Mod 3 Analysis: Analyze the paired data set using all the methods we talked
about in module 3.
a. Create a scatterplot, with the regression line.
b. Create a residual plot of your data about the regression line and describe it.
c. What is the slope of the prediction equation, including units? Interpret this
slope in the context of the data.
d. What is the y-intercept of the prediction equation? Interpret this yintercept in the context of the data.
e. What is the correlation coefficient and what does this tell us?
f. What is the coefficient of determination, r2?
g. What is the standard error of regression, Se?
3. Mod 4 Analysis: Now find the two best non-linear models (Quadratic,
Exponential, or Logarithmic) of the curve that models your data set. For each of
the nonlinear equations:
a. Create a scatterplot, with the regression curve.
b. Create a residual plot of your data about the regression curve and describe
it.
c. What is the coefficient of determination, r2?
d. What is the standard error of regression, Se?
4. Overall Analysis: Provide a table or chart that compares the residual plots, r2,
and Se of all your models. When comparing your residual plots, you will need to
incorporate a slide with all 3 of your labeled residual plots to provide a visual for
the comparison.
Use your table of these criteria to determine which of your models you think
would be the best to use and why. Based on your best fit model, interpret both
the r2 and Se in the context of your data.
Consider the following table we used in class as an example:
Model
Residual Plot
se
r2
(vs. x-values or Versus Fit)
++
What do you see? Describe the shape.
(U - Shaped, Downward U-Shaped, SShape, Band, Oval, etc.)
Linear**
Exponential
Quadratic
Logarithmic
Addressing the criteria for regression modeling, discuss each criteria and whether
the best fit model meets or does not meet each of the criteria. You must provide
the necessary graphs in your discussion of the criteria.
5. Submitted Information: You need to email professor Grigoryan your data set
saved as an excel file and your completed project that you are presenting to the
class BEFORE anyone presents.
6. Presentation: You will also be graded on your presentation as well as your content.
Your eye contact, enthusiasm, and overall presentation will be factored into your
grade. You will lose points if you are a disrespectful listener as well. If you are
not listening intently for others’ presentations, you will lose points.
Criteria for regression modeling:
How good is the fit of the regression model?
When assessing how well the linear or non-linear regression model fits the data, we examine the
following criteria:
1) The linear regression model must have two quantitative variables.
2) The form of the scatterplot is linear or curvilinear. Take note that the scatterplot follows the
trend of the model you are attempting to use.
3) Identify outliers and discuss whether they may be overly influential outliers. In the case of
overly influential outlier, you may need to remove these points and re-examine your model.
Tentative Grading Rubric (This rubric is subject to change)
Section/Points
Required
Materials
3
3
Comments:
Introduction:
2
2
2
2
1
Comments:
Linear Model:
4
4
2
9
2
9
1
5
1
1
Details that need to be covered
Check Off
Before Project Presentation (6 Points)
Emailed Professor Grigoryan data set in excel format (.xls) BEFORE
anyone has presented.
Emailed Professor Grigoryan completed project in PowerPoint (.ppt) or
other approved format BEFORE anyone has presented.
Total Points:
Information to setup your presentation. (9 Points)
Title slide with your project title, class, semester, year and instructor
name.
Background and/or related information about your topic.
Explained what your Explanatory and Response variables are specifically.
Provided a scatterplot, large enough for everyone to see, of your
variables without a regression curve.
Provided what models you will be using to analyze your data. (Linear,
Quadratic, Exponential Decay, Exponential Growth, Logarithmic)
Total Points:
Module 3 Analysis (38 Points)
Provided a scatterplot, with the regression line, large enough for
everyone to see during the presentation. Graph is labeled appropriately.
Provided the linear regression equation, large enough for everyone to
see during the presentation.
Provided a residual plot, large enough for everyone to see, of your data
about the regression curve and describe it in your presentation.
Provided the slope of the prediction line with the appropriate units.
Interpreted the slope of the prediction line in the context of the data,
as we did in class.
Provided the y-intercept of the prediction equation as a coordinate.
Interpret the y-intercept of the prediction equation in the context of
the data, as we did in class.
Provided the correlation coefficient.
Provided an analysis of what the correlation coefficient tells us.
Provided and stated aloud in presentation the coefficient of
determination, r2. (You are not interpreting this value at this time.)
Provided and stated aloud in presentation the standard error of
regression, Se? (You are not interpreting this value at this time.)
Comments:
Total Points:
Non-Linear
Model #1:
4
3
1
1
Non-Linear
Model #2:
4
3
1
1
Module 4 Analysis (9 Points)
Provided a scatterplot, with the correct regression curve, large enough
for everyone to see during the presentation. Graph is labeled
appropriately.
Provided a residual plot, large enough for everyone to see, of your data
about the regression curve and describe it in your presentation.
Provided and stated aloud in presentation the coefficient of
determination, r2. (You are not interpreting this value at this time.)
Provided and stated aloud in presentation the standard error of
regression, Se? (You are not interpreting this value at this time.)
Module 4 Analysis (9 Points)
Provided a scatterplot, with the correct regression curve, large enough
for everyone to see during the presentation. Graph is labeled
appropriately.
Provided a residual plot, large enough for everyone to see, of your data
about the regression curve and describe it in your presentation.
Provided and stated aloud in presentation the coefficient of
determination, r2. (You are not interpreting this value at this time.)
Provided and stated aloud in presentation the standard error of
regression, Se? (You are not interpreting this value at this time.)
Comments:
Conclusion:
5
3
5
4
4
1
Comments:
Participation:
7
Miscellaneous:
Total:
Overall Analysis: (22 Points)
Provide a table with the model type, residual plot description, r2 and Se
Provided a slide with all three residual plots to discuss and analyze.
Talk about which of the models presented is the best fit for your data
and why.
Interpreted the r2 of the best fit model.
Interpreted the Se of the best fit model.
Concluding statement to wrap up your presentation.
Total Points:
Audience: (5 Points)
Attentive listener to others. Present for all presentations.
Additional deductions.
Grade: