Multivariate Statistical Analysis

Project Goal

The goal of this project is to apply the material introduced in this class on a real world dataset and create a formal report to represent all your work.

Instructions

1. Write a summary and description of the dataset you use. You may use summary statistics, descriptive statistics, figures, etc.
2. Clearly state the research questions and your analysis plan of the project.
3. State at least two possible multivariate analysis methods you want to use in the analysis. You may also use other multivariate analysis methods that’s not covered in the book to do your analysis.
4. State whether your data satisfy the assumptions, how you fit the models, how you use the output from these models to answer your research questions (include numerical summaries, graphics and interpretations).
5. Compare the models or further discussion of your models, provide suggestions to other people if they want to use similar methods in their analysis.
6. Address any problems you meet while analyzing the dataset.
7. Discuss further questions raised by your study (that might be investigated in future research or analysis).

Report

Your grade on this project will be based on a written report. You should write up your analysis and report in the form of a short technical/research article, preferably in the format of a high-quality statistics or mathematics journal.

• Your main part of report should be 4-7 pages, your writeup should briefly describe the points listed in the previous section.
• An abstract highlighting the results from your analysis should be included before the body of the work.
• Graphs, figures and tables can be helpful to understand your findings. An appendix should include your code and/or computer output. (extra figures/tables and appendix are not included in the 4-7 page limit)
• Interesting computer results can be included in the body of the report.
• A bibliography is necessary for any references you make.

Grading

• 20% Description of the data, summary of the possible research questions
• 40% Appropriate and correct analysis procedures
• 20% Complexity of your decisions, suggestions, extensions
• 10% Well-written and attractive report meeting the guidelines set above
• 10% Spelling, grammar and punctuation

Dataset: House Price Data

dat <- read.csv("C:/Users/poster/Desktop/kc_house_data.csv", header = TRUE)

df.num <- dat %>% select(price, floors, bedrooms, bathrooms, sqft_living, sqft_living15, 
    sqft_lot, sqft_lot15, sqft_above, sqft_basement, lat, long, zipcode, yr_built, 
    yr_renovated, waterfront, view, condition, grade)

## Partitioning numeric data to explore more thorougly

# Home Attributes
df.home <- df.num %>% select(price, floors, bedrooms, bathrooms, yr_built, yr_renovated, 
    view)
# Square Footage Measures
df.sqft <- df.num %>% select(price, sqft_living, sqft_living15, sqft_lot, sqft_lot15, 
    sqft_above, sqft_basement)
# Location info
df.loc <- df.num %>% select(price, lat, long, zipcode, waterfront, condition, 
    grade)