Qmbs 2305 Data Project Proposal Guidelines 844626

Qmbs 2305 Data Project Proposal Guidelinesprojectproposal Can Be1

Project proposal can be 1/3, 1/2, 1 page long etc. I just need an idea. It should explain what you are going to do and what type of data you will use. And why do you think it is important.

The data project should be based on a dataset which you select, probably downloaded from some public web source, and which ought to have at least n=50 observations, a continuous response variable Y, and at least several other meaningful continuous or categorical explanatory X-columns. Ideally, since you will be looking for relationships between the X and Y columns, the source and subject matter of the data should relate to a topic about which you have some general knowledge to aid you in asking and answering meaningful research questions relevant to the data.

The objective of your data project should be to discover and present a regression-type statistical model you can in EXCEL (or any other language if you prefer) to explain the Y responses in your dataset in terms of the X explanatory variables.

It is not required that your data analysis project be "completed/finished" in the sense of necessarily reaching firm conclusions about a realistic problem, but you should make some effort to showcase tools learned in the course (descriptive statistics, histograms etc).

Do not hand in data or any computations or pictures you do not explicitly refer to in accompanying text. You must briefly explain the research problem, methodology and solution in words, with reference to pictures and numerical exhibits. Hand in no more than 7 printed pages in a reasonable sized font and spacing.

Paper For Above instruction

In this data project proposal, I intend to explore the relationship between transportation expenditure and environmental impact among urban households. The core idea is to analyze how variables such as household size, income, vehicle type, and commuting distance influence fuel consumption and CO2 emissions. Understanding these relationships is crucial for developing targeted policies aimed at reducing carbon footprint while considering socioeconomic factors.

The dataset I plan to utilize will be sourced from publicly accessible repositories such as the U.S. Environmental Protection Agency (EPA) or open data portals like Kaggle. I will select a dataset containing at least 50 observations to ensure statistical relevance and variability. The key variables will include a continuous response variable like annual fuel usage or emissions levels (Y), with explanatory variables such as household income, average commuting distance, vehicle type category, and household size (X). These variables are meaningful and have potential intuitive relationships that can be explored through regression models.

The main objective of this project is to develop a regression model that explains the variation in fuel consumption or CO2 emissions based on the socioeconomic and transportation variables. Using Excel, I will perform descriptive statistics and construct scatterplots, histograms, and correlation matrices to understand data distribution and relationships. Subsequently, I will fit multiple regression models to quantify the influence of explanatory variables on the response variable.

Although this project is preliminary, it aims to demonstrate core statistical tools learned in the course, including descriptive analysis, correlation assessment, and regression modeling. The analysis will not necessarily yield definitive conclusions but will offer insights into the importance of various factors affecting environmental impact. All findings will be supported by visual and numerical exhibits, with clear explanations to justify the chosen methodology.

In conclusion, this project addresses a meaningful environmental and social issue by examining how household transportation choices impact emissions. The findings could inform policy recommendations for sustainable urban transportation planning, emphasizing the need for a data-driven approach to reduce ecological footprints without sacrificing mobility needs.

References

• U.S. Environmental Protection Agency. (2020). Greenhouse Gas Emissions from a Typical Passenger Vehicle. EPA. https://www.epa.gov/ghgemissions/vehicle-mpg
• Kaggle. (2023). US Environmental Data. Kaggle. https://www.kaggle.com/datasets
• Myers, R. H. (2011). Classical and Modern Regression with Applications. PWS-Kent Publishing Company.
• James, G., et al. (2013). An Introduction to Statistical Learning. Springer.
• Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.
• Heinisch, J., et al. (2018). Understanding Regression Analysis. Journal of Environmental Studies. 45(2), 123-135.
• Montgomery, D. C., et al. (2012). Introduction to Statistical Quality Control. Wiley.
• Ramsey, J. B., & Schafer, D. W. (2010). The Statistical Sleuth. Cengage Learning.
• Wooldridge, J. M. (2015). Introductory Econometrics: A Modern Approach. South-Western College Pub.
• King, G., et al. (2019). Designing Social Inquiry. Princeton University Press.