I am evaluating a scenario's output parameter's dependence on three parameters: A, B and C. For this, I am conducting the following experiments:
- Fix A+B, Vary C - Total four sets of (A+B) each having 4 variations of C
- Fix B+C, Vary A - Total four sets of (B+C) each having 3 variations of C
- Fix C+A, Vary B - Total four sets of (C+A) each having 6 variations of C
The output of any simulation is the value of a variable over time. For instance, A could be the area, B could be the velocity and C could be the number of vehicles. The output variable I am observing is the number of car crashes over time.
I am trying to determine which parameter(s) dominate the outcome of the experiment. By dominate, I mean that sometimes, the outcomes just does not change when one of the parameters change but when some other parameter is changed even by a small amount, a large change in the output is observed. I need to capture this effect and output some analysis from which I can understand the dependence of the output on the input parameters. A friend suggested Sensitivity Analysis but am not sure if there are simpler ways of doing it. Can someone please help me with a good (possibly easy because I don't have a Stats background) technique? It would be great if all this can be done in R.
Update: I used linear regression to obtain the following:
lm(formula = T ~ A + S + V) Residuals: Min 1Q Median 3Q Max -0.35928 -0.06842 -0.00698 0.05591 0.42844 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.01606 0.16437 -0.098 0.923391 A 0.80199 0.15792 5.078 0.000112 *** S -0.27440 0.13160 -2.085 0.053441 . V -0.31898 0.14889 -2.142 0.047892 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1665 on 16 degrees of freedom Multiple R-squared: 0.6563, Adjusted R-squared: 0.5919 F-statistic: 10.18 on 3 and 16 DF, p-value: 0.0005416
Does this mean that the output depends mostly on A and less on V?