Okay, let's evaluate each modeling equation candidate based on the r^2 value:
a. y=16.5839•1.0185^x
No r^2 value given, so cannot determine if this is the best fit.
b. y=0.985515x-2.81333
No r^2 value given for this linear model, so cannot determine if it is the best fit.
c. y=-0.0000758936x^3+0.0143444x^2+0.207549x+7.70667
This is a 3rd order polynomial model, but no r^2 is given, so cannot determine if it is the best fit.
d. y=0.00182197x^2+0.785098x+1.195
If this model has the highest r^2 value, it would be the best fit.
Based on the information provided, the only option that could potentially be the best fit is choice d, the quadratic model y=0.00182197x^2+0.785098x+1.195, if it has the highest r^2 value. But without the actual r^2 values for each model, a definitive determination cannot be made.
Does this help explain the approach? Let me know if you have any other questions!