Sensitivity Analysis & Model Diagnostics Case Solution
The data has been provided in the form of an excel file named “Problem Set 7 tree”. Which was then used to conduct the sensitivity analysis using Senit Excel Add-in. which is used to drive the sensitivity of the dependent variables with respect to the independent variables. However, two different sensitivity analysis has been conducted, where the dependent variable is constant for both referred to as P (YV). Furthermore, the independent variable for first analysis has been taken named as No research. Secondly, the independent variable has been taken named Research. To evaluate the sensitivity do dependent variable P (YV) against the Research and no research conducted by Dustin.
The graph above illustrates the independent variables Research and no research plotted along the x-axis and y-axis of the graph. However, it can assessed that the sensitivity of the value of the variables are also represented in the graph above. Furthermore, it can be determined that, the in best interval for Dustin to conduct his research, can be evaluated. Which shows, that the best interval in between 40% and 58%, as the research line is above the no research line in the graph above. Which depicts that the EV of the research is better than the EV of no research between this intervals. Hence, it is recommended to Dustin that the research should be conducted between theseintervals.
The sensitivity Analysis is a technique used to evaluate, how the values of an independent variable effect the values of dependent variables under a set of Assumption taken initially. Which, in turn, would enable the management of the user of the information to determine, just how sensitive a dependent variable is with respect to the independent variable present in the assumption. Furthermore, it would also help them to determine, which Independent variable has more impact on the dependent variable compared to others.
Relative Usefulness of Sensitivity Analysis
The sensitivity Analysis is a useful tool, which is used to determine the level of sensitivity of a dependent variable with respect to their independent variables. Therefore, the analysis provides its user with information regarding the effect of changes in one or more independent variables on the dependent variable.
Using R and Rattle
The data has been provided named as “Mopps_with_commas.csv”, which was then used to conduct Tree model analysis in Rattle. However, Rattle is downloaded through R-Studio. Furthermore, the tot_success had been taken as the dependent variable. Moreover, other variable including Mkt_Class, biz_type, Sales_type, Num_type, Past_expan and change_hands are taken as independent variable. Where the dependent variable depends on the independent variables.
The cross-validation tool was used to effective estimate the test error of a predictive model. Furthermore, it creates partitions in a sample of observations known as validation sets. After conducting the analysis of the dataset provided and fitting am appropriate model. The performance could be measured against each validation set created, which could enable its user to gain a better understanding and esti8mate how the model would perform, while predicting new observations. However, cross-validation is an effective tools to test the model’s performance…………………
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