model parameters using sum square difference minimization

[Josh_Metro]

I'm new to Alteryx, and I'm trying to replicate something that takes me only a few seconds to do in Excel. 

I'm try to fit a dataset to a model. In Excel, I do it this way.

This is the model (coefficients in bold): C = (Co-D) * e^(-k*t)

I set the starting values from the parameters

D	1
k	1

These are are my observations and my calculated C.

Co	t	C - observed	C -calculated
0	3	1.5	2
0.5	3	1.25	1.213
2	3	1.16	0.271
5	3	0.79	0.013
10	3	0.45	9E-05
30	3	0.02	2E-13
60	3	0.06	2E-26

I then calculate the sum square difference between the observed and calculated C values using the SUMXMY2 formula in Excel

SSD

1.85167928

Then I use the Solver to minimize the SSD by changing D and k. That literally takes seconds to do, and I get the best fit parameters for my dataset. How can this be done in Alteryx. I uploaded an example.

[Josh_Metro]

Hi Rajdaiya. I'm happy to share. I've attached a sample workflow.