Final answer:
After initial analysis, a management scientist may conduct a sensitivity analysis to anticipate how recommendation changes may occur with future variations in model parameters. This involves identifying variables, plotting data, calculating regression and correlation, and determining linear relationships and significance.
Step-by-step explanation:
After a basic analysis, a management scientist may perform a sensitivity analysis to determine how the recommendations may vary if any of the model parameters changes in the future. Sensitivity analysis examines how different values of an independent variable affect a particular dependent variable under a given set of assumptions.
When performing a sensitivity analysis, the typical process involves:
- Identifying the independent and dependent variables.
- Drawing a scatter plot to visualize data points.
- Using regression to find the line of best fit and calculating the correlation coefficient to measure the strength and direction of the linear relationship between two variables.
- Interpreting the significance of the correlation coefficient to determine if the relationship is statistically significant.
- Assessing if there is a linear relationship between the variables based on the scatter plot and correlation coefficient.
By analyzing the slope and y-intercept of the regression line, we can understand the average change in the dependent variable for each unit change in the independent variable and the expected value of the dependent variable when the independent variable is zero, respectively.