Quantitative Analysis for decision making class:
Please respond the following problems as to complete as per 200 word for each problems asked. Please cite references appropriately.
1) Linear regression is great tool for use in statistics and most research applications. Many companies use it to develop forecasting models and to optimize resource utilization. However, using it in the logistics and supply chain is more difficult because of many variables, such as labor, demand, inventory, multiple suppliers, etc. How do you determine which variables are more important for your forecasting model and how do you know that your forecast is accurate?
2) Outliers can help researchers to determine the cause of the faulty measurements (process or measurement tool failure). Based on the grouping of outliers, researchers can deduce if the results are "false positives" or if the error is part of the process setup. What are some of the possible consequences of assuming that outliers are "false" positives? How can you reduce number of outliers in your study?