First you'd have to select features, check out:
https://www.mathworks.com/discovery/feature-selection.html
for info and/or ideas on how to do this.
You'll also need to decide what kind of model you're using, for the standard multi-linear regression check out the documentation on regress()
https://www.mathworks.com/help/stats/regress.html?s_tid=srchtitle
Now - if you really want the simplest model, I'd say you could skip feature selection and just run:
regress(labels, features)
and this will give you the regressor for each feature. Now your model is simply
prediction = A1*feature1 + ... + AN*featurenN
But I would generally advise against this, mainly becasue of the danger of overfitting. I suggest building a train and test based algorithm
