代做dataset、代写statistical model、Python程序语言调试、代写Python-代写Python编程

Project

Up to two students are allowed to work together on one project. However, each person has to

contribute equally to the project. If enough evidences show other way around, interviews may be

needed to judge how the project is carried out and marks will be deducted accordingly (to the

person done less work).

It is important to treat your projects like a real project requested by a customer/client. This means

that there is no absolutely correct or wrong answer. It is completely up to you to make your own

judgments and choose a proper statistical model. Your projects should have three components:

• Part I : Background, objectives, and your conclusions of statistical analyses (one or two

pages).

• Part II: This is your main body. Give details of your statistical analyses, including some

intermediate results.

• Part III: R/Python/SAS codes and outputs.

The evaluation of your written projects in part is based on

a. How do you understand the nature of datasets involved?

b. How do you handle the problems coming from the datasets?

c. What is the reasoning to choose a particularly statistical methodology for analysis?

d. Is your statistical methodology valid for chosen datasets?

e. How do you check and refine your statistical models?

f. Does your analysis answer or partially answer the problems?

g. What is your concerning regarding your analysis?

h. What are your suggestions for further analysis?

Here are some steps you may find useful.

1. Data descriptions.

2. Some preliminary analyses/data exploration.

3. Choose proper transformations if needed.

4. Model building (model assumptions, initial model buildup, model adequacy (diagnostic

tests), model revisions, etc).

5. Summary of statistical findings (model inferences).

6. Further discussion.