Keeping Track of the Adoption in the U.S.

The final report should have four parts:
1. Introduction: A discussion of what questions you are interested in and your motivation for this research.
2. Data Set: Describe details about the variables in the data set and your data source.
3. Analysis: Describe how you used multiple regression to analyze the data set and discuss your inferences based on your final model. Specifically, you should discuss how you carried out the steps in analysis discussed in class, i.e., exploration of data to find an initial reasonable model, checking the model, and development and analysis of your final model,
4. Conclusion: Provide brief conclusions about the results of your study.
See the attachment for detail.
Please use the data link in the project proposal attched to finsh the analysis(using SPSS)
Please read the two files attached carefully.
You can put the output in the report.

Statistic method project proposal: Date to analyze: Archery at 2008 olympic Data link: https://dasl.datadescription.com/datafile/archery/?_sfm_cases=40+500 (use the link above to access data) Data description: In Olympic Archery both men and women start with a field of 64 qualifiers. Each archer shoots a round of 72 arrows (total possible score: 720) to establish a seeding position. Then they participate in a single-elimination contest. Thus, the seeding round is the only one that provides data for all archers (because some are eliminated at each step of the elimination rounds). The data are the seeding round data for the 2008 Olympics. The SPSS software analysis should consist model summary, Anova, coefficients, etc. An example output screenshot is shown below.

Students are required to complete a project related to the course objectives. Each group can have 2-3 members. The project will be developing a real-world problem using a real data set of interest to you. The deliverables of the project include project draft and a final report.

Your group should HAND IN ONE PROJECT PROPOSAL (with all group members’ names) by Nov 25, 2019. It should include the data set you plan to analyze, and one paragraph describing your dataset and your topic of interest. The completed final report must be submitted electronically on LMS/BB by Dec 16, 2019.

Project Description

You can choose any topic you like. But you need to use multiple linear regression analysis to analyze your data set. This indicates that you have to examine the relationships of one dependent variables and multiple independent variables.

The final report for the project should be a 5-10 page paper that includes the following sections:

1. Introduction: A discussion of what questions you are interested in and your motivation for this research.
2. Data Set: Describe details about the variables in the data set and your data source.
3. Analysis: Describe how you used multiple regression to analyze the data set and discuss your inferences based on your final model. Specifically, you should discuss how you carried out the steps in analysis discussed in class, i.e., exploration of data to find an initial reasonable model, checking the model, and development and analysis of your final model, 
4. Conclusion: Provide brief conclusions about the results of your study.

Data Sets

Examples of questions of interest are as follows: What properties of a baseball team best predict its success over the course of a season?  What properties of a college are related to its rank in the U.S. News and World Report rankings? Is the gas mileage of an automobile predictable from properties such as weight, horsepower, and so on? Is the unemployment rate related to economic measures such as interest rates, stock returns, and the inflation rate?

You will need a data set to explore your question of interest. The data set should ideally contain at least 30-50 observations (e.g., companies, people, countries, etc., as the case may be), and at least 4 variables (pieces of information about the observations; e.g., stock price, revenues, profits, salaries, gender, etc). One of the variables, the dependent variable, should be a numerical variable that you want to model or forecast (e.g., for the examples above, team winning percentage, stock price change, U.S. News and World Report rank, gas mileage, and unemployment rate respectively).

The Data and Story Library (DASL) has many interesting data sets you may want to use

http://lib.stat.cmu.edu/DASL/