Function for the equation

Is there any way to define a function for this equation? ln(𝛽/(T^2))=−𝐸/𝑅*1/𝑇+ ln(𝑅/𝐸 * 𝐴)

I am tryting to use different spreadsheets of an excel to calculate this function.So i would like to know how to write code as a function to apply it to all different spreadsheets

[KSSV]

What are known values here? Read about anonymous function.

[Betty Johnson]

The unknowns are E and A and I need to import different datasets on different sheets of Excel to find these two values

[Walter Roberson]

Is this a curve fitting question then? You have a spreadsheet with multiple sheets, one holds beta, one holds T, one holds R, and you would like to find the best single E and A values that model the system?

[Betty Johnson]

No all the speadsheets have the list of values for all parameters except fot E and A and I need to find E and A for all those sheets.So I am trying to define a function which would make the task easier

Answer 1

[Walter Roberson]

If you have

log(beta/T^2) = -E/(R*T) + log(R*A/E)

and you need to solve for R, then

R = E ./ (T .* lambertw(T .* A ./ beta))

If instead you need to solve for T then

%under the assumption that A, beta, E, R are all non-negative !!
inner = sqrt(E*A/(R*beta))/2
outer = E/(2*R);
T = [outer/lambertw(inner), outer/lambertw(-inner)]

Depending on the exact values involved, the first of the two T entries might be complex

If any of A, beta, E, R, might be negative, then the inner becomes more complicated,

inner = sqrt(E.^2./(R.^2.*beta)).*sqrt(R.*A./E)/2

[Betty Johnson]

The unknowns are E and A

[Walter Roberson]

xlsfile = 'YourFileName.xlsx';

[status, sheetnames] = xlsxinfo(xlsfile);
if isempty(status)
   error('cannot read file "%s" as an excel file', xlsfile);
end
numsheets = length(sheetnames);

EA = zeros(numsheets,2);

for K = 1 : length(sheetnames)
  thissheet = sheetnames{K};
  Tab = readtable(xlsfile, thissheet);
  beta = Tab{:,1};   %adjust column numbers as appropriate
  T = Tab{:,2};
  R = Tab{:,3};
  guess = [1,3/4];
  residue = @(E,A) sum((-E./(R.*T) + log(R.*(A./E)) - log(beta./T.^2)).^2,1);
  EA(K,:) = fminsearch(residue, guess);
end

I do not have your file to test with so I have no idea how close to the global minima the above will get.