Quantitative Analysis for decision making class
1- linear regression is great tool for use in statistics and most research applications. Several companies use it to develop forecasting models and to optimize resource utilization. Thus, using it in the logistics and supply chain is more difficult because of many variables, such as labor, demand, multiple suppliers,inventory, etc. How do you evaluate which variables are more important for your forecasting model and how do you know that your forecast is accurate?
2- outliers will help researchers to determine the cause of the faulty measurements (process or measurement tool failure). Based on the grouping of outliers, researchers will 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 considering that outliers are "false" positives? How will you reduce number of outliers in your study?