Research Question: Ken Donley, Aaron Buchholz, Gregory Fland

Research Questionken Donley Aaron Buchholz Gregory Flanders Robert

The United States Federal Government is building new buildings throughout the United States. The buildings on the East Coast seem to cost more to build then the West Coast. The Congress and Senate want to know if the buildings size is the factor for the increase of cost. Before Congress and the Senate approves the funding for more buildings they want to know why a building price is inconsistent with size.

Research Question: Is there a correlation in price (dependent variable) based on size (independent variable)? Variables include price (numeric) and size (numeric). The null hypothesis (H0) states that there is no correlation between price and size, while the alternative hypothesis (H1) suggests that there is a correlation between these variables.

Paper For Above instruction

The question of whether building size correlates with construction costs is a significant concern for federal agencies and policymakers aiming to optimize budget allocation for infrastructure development. Understanding the relationship between the size of a building and its cost can inform strategic planning and resource management, especially when discrepancies in costs are observed across geographic regions such as the East and West Coasts of the United States.

In conducting this research, it is essential to analyze the data through rigorous statistical methods that can establish whether a meaningful relationship exists between building size and cost. The primary variables of interest in this study are the building’s size and the associated construction cost, both measured numerically. The hypothesis testing framework involves a null hypothesis (H0) claiming no correlation exists, versus an alternative hypothesis (H1) proposing a significant correlation.

The initial step involves collecting data on a representative sample of federal buildings from both coasts, ensuring data on their size and total construction cost are accurate and reliable. Descriptive statistics will provide a preliminary understanding of the data distribution, highlighting any potential outliers or skewness that could influence the analysis.

To examine the potential correlation, a Pearson correlation coefficient analysis will be conducted. This statistical measure evaluates the strength and direction of the linear relationship between the two variables. A coefficient near +1 or -1 indicates a strong positive or negative correlation, respectively, while a coefficient near zero suggests no linear relationship.

Further analysis could involve regression modeling to estimate how much of the variation in building costs can be explained by size variations. This approach provides insights into the cost implications of constructing larger buildings and helps determine whether size alone can predict costs accurately or if other factors are influencing the prices.

Understanding these relationships is further complicated by geographic region. The observed cost differences between East and West Coast buildings might be attributable to regional economic factors, labor costs, material availability, or regulatory environments. Therefore, incorporating regional dummy variables into regression models or conducting subgroup analyses can clarify whether the size-cost relationship varies regionally.

Additionally, factors such as building type, design complexity, and construction technology should be examined, as they might confound the relationship between size and cost. Multivariate analysis techniques, such as multiple regression, could control for these variables, providing a more comprehensive understanding of what drives construction expenses.

Ultimately, the findings from this analysis will guide federal decision-makers regarding budget expectations and policy development for future building projects. If a significant positive correlation exists, it reaffirms the expectation that larger buildings tend to incur higher costs, but if no correlation or a weak correlation is found, it suggests that other variables significantly influence building costs, necessitating further investigation.

In summary, establishing whether a correlation exists between building size and cost involves careful data collection, statistical testing, and interpretation within regional and contextual parameters. The insights generated will contribute to more strategic fiscal planning and resource allocation in federal construction projects, ensuring better cost management and project outcomes.

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