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ECO520 Business Analytics Tools II: Final Project Guideline
The
main purpose of the project is to apply the business analytics tools to your
own topics. Throughout the class, we covered many interesting aspects of
descriptive and predictive analytics using SAS. The grades are based on the
creativity, proper methodologies, pro ciency in understanding of the models and
in explanation for the general audiences, and program ability to perform on predictive
analytics.
- Data
Sources
Students chose their own data at the beginning
of the class
- Timeline
for the project:
10-15 Minutes Presentation
and 5 minutes discussion on 6/4 (Tuesday) Suggested items on the presentation
(All in Powerpoint Slides), Try to list all items regardless nished or not
{ Introduction of topic and data
{ Purpose of the study and
Expected bene ts { Descriptive Analytics
Proc mean, Proc summary,
Proc Uniariate, Proc sgplot of gplot, and Maps
1
| Final Prject Guideline | ECO520 |
Correlation analysis and
Analysis of Variance (ANOVA) Other relative analyses including histograms and
statistics
{ Predictive Analytics
Ideas or required estimation
Final Project Due by 6/15
(Saturday) 10:00 PM (A PDF le format Electric Submission to Dropbox in D2L with
all relevant data and sas or R codes)
- Outline
of the Paper
Your
paper should consist of the following 8 sections. With each section, I give
some guidance about what should be included therein and the approximate page lengths
you should be thinking about for each section.
3.1 Introduction (1 to 2 pages)
Motivate your project
question, i.e. brie y tell me why the broad area you will be looking at is
interesting/important.
State your
question and tell me (if it is not totally obvious) how this question ts into
the broader area you mentioned above.
Very brie y describe how
you will try to answer this question (data, methods) and what you nd (results).
3.2 Data (1 to 2 pages)
Describe the data you will
be using in this analysis. Brie y describe the data including sample periods.
Give summary statistics of your data.
- This should be presented in
a Table and/or graphs. Explanation includes general time trend, historical
events that might be interested in the estimation, and recent movements, etc.
Page 2
| Final Prject Guideline | ECO520 |
3.3 Empirical Methodology (1 to 2 pages)
Describe your estimating
equation(s) in words and in math (i.e. include the exact regression equation(s)
in this section).
Describe how the
methodology is going to help you answer your question comparing to a regression
model.
3.4 Results (3 to 4 pages)
Present your results with
tables and/or graphs/charts (not raw output from SAS or R please).
Descriptive Analytics
{ Proc mean,
Proc summary, Proc Uniariate, Proc sgplot of gplot, and Maps
{ Correlation analysis and
Analysis of Variance (ANOVA) { Other relative analyses including histograms and
statistics
Predictive
Analytics (All required to apply to your model) { Clustering Analysis
{ Regression Model with Groups based on
Clustering
{ Simple, Multiple
Regression on linear or nonlinear models { Discrete Probability Model :
Logistic Model
{ Machine Learning techniques: Random Forest,
Neural Network Analysis
Describe your results in words (both the signs
and magnitudes).
The emphasis
should be on coe cients that relate to your research question, but you may
mention others. Certainly, you do NOT need to describe (in words) ALL of the
coe cients, just the important ones.
Performance your model depending upon the
predictability. You need to show the strength of your model by comparing other
alternative models. The performances of models should be measured using a test
data set, that was not used to estimate the main model.
Page 3
| Final Project Guideline | ECO520 |
3.5 Summary of Project (1 to 2 pages)
Summarize everything brie y
(i.e. in one paragraph you should be able to state your project question,
empirical approach, and results).
Potential shortcoming of your project and
desirable future works.
3.6 Bibliography (1 page)
Any related work with your work
3.7 Appendix: SAS or R command le
Include
all SAS or R commands used to generate the output. Codes and Data needs to be
included in separate les. Make sure all submitted SAS or R codes without any
errors. There will be very high penalty if they are not working with errors.
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