It's not entirely clear what you are asking: are you asking how to perform a linear regression on the dataset formed by combining both sets of measurements and times? (By the way: it looks like there are only seven values in your second set of time stamps.) If so, just combine the time and measurement vectors and use Matlab's "polyfit" like in the following example. I'm making "A" your measurements from instrument 1 and AT your time stamps from set 1, and "B" your measurements from instrument 2 and BT the time stamps from instrument 2:
figure;
% Time vector, set 1
AT = 38:42;
% Measurements set 1 (simulated)
A = randn(size(AT)) + 0.1*(38:42); % Noise plus some slope
% Time vector, set 2
BT = (40:47) + 0.5;
% Measurements set 2 (simulated)
B = randn(size(BT)) + 0.1*(40:47); % Noise plus same slope
% Combine measurements and times
measurements = [A, B];
times = [AT, BT];
% Plot
plot(times, measurements, 'k.');
% Linear regression
P = polyfit(times, measurements, 1);
% Plot result
hold on;
plot(times, polyval(P, times), 'k');
hold off;
P has your regression coefficients, P(1) will be the slope (should be about ~0.1, gets better with more measurements) and P(2) should be the offset.
