Project should address a real-world problem with relevant data Use your own data if available

Project should address a real-world problem with relevant data
Use your own data if available
Otherwise, use secondary data, but you need to provide the source. Goal of project is to show that you know the material from the course,
not to find a significant hypothesis testing result. A rigorous study covering the materials discussed is more important than
showing that the test result is statistically significant. Page limit is 15, including figures and computer output (only include
necessary computer output, if any)
• Final report should consist of
➢Objective (e.g. verify a hypothesis or theory, etc.)
➢Brief description of data and source
➢Methods and models used to analyze the data
➢Results of analysis and practical implications
➢Limitations of the data, methods, result.
• Misuse of methods
• Misinterpreting results
• Overlooking model inadequacy
Data Analysis
• Don’t forget data cleaning steps
➢Missing data, outliers, etc.
• Why did you use the method that you chose?
• What are possible limitations of the data, method, results – discuss
these and techniques for addressing
• Include diagnostics for your methods.
Statistical Tool to be used : JMP SAS
Statistics topics discussed in course : organizing a statistical problem, descriptive statistics, distributions, Normal distributions, Relationships – correlation, simple linear regression, multivariate relationships, time series, Framing the research question, developing a hypothesis
Producing data for scientific studies – experiment design, Producing data for social sciences – survey design, ethics in data collection (IRB), Inference for quantitative data – inference about a population mean, hypothesis testing, two-sample problems, Inference about linear relationships – multivariate regression, moderation and mediation, Multivariate methods – exploratory factor analysis, principal components analysis , Inference for categorical data – inference about a population proportion, comparing two proportions, Inference about categorical relationships – chi square test, logistic and ordinal regression, multiple means comparisons