For a new project budget, I would select regression analysis as my method for estimating costs. Regression analysis is better than the high-low method because it uses all available data points instead of just two extremes, offering more accurate estimates. It also considers multiple cost drivers at once, unlike simpler methods that only focus on one variable. While learning curve analysis is useful for repetitive production processes where efficiency improves over time, regression analysis provides more flexibility for estimating various project costs. To apply the six steps of cost estimation: 1. Define the estimation goal: Clearly specify which project costs require estimation and their role in the budget. 2. Select cost drivers: Pinpoint variables that greatly influence costs, like labor hours, materials quantity, or production volume. 3. Collect relevant data: Gather historical data from similar projects, ensuring it is reliable and representative. 4. Plot the data: Generate scatter plots to visualize the relationships between cost drivers and total costs. 5. Select and apply the estimation method: Develop a regression model of the form: where represents total cost, is fixed cost, and are coefficients for each cost driver. 6. Evaluate and refine the model: Test the model's accuracy using statistical measures and adjust as needed. Potential challenges include data quality issues, identifying the proper cost drivers, assuming linearity, managing outliers, interpreting the m