Save regression output (fitlm) into table

Hello,

Is there any way to save the output from multiple linear regression into a table? (I don't mean exporting it into excel).

So for example, if I use the patients data set and calculate regression of weight and age on diastolic blood pressure, is there a way to save "Model_1" into a table (a table within matlab)? I realize that the output already looks like a table, but I mean an "actual" table (something like table (a, b, c, ..... ) ).

Many thanks!

P.S.: this is not a homework assignment, I just want to know if it's possible and if so, how to do it :)

load patients Weight Age Diastolic

x = [Weight, Age];
Model_1 = fitlm (x, Diastolic, 'VarNames', {'Weight', 'Age', 'DBP'})

Model_1 = 
Linear regression model:
    DBP ~ 1 + Weight + Age

Estimated Coefficients:
                   Estimate       SE        tStat       pValue  
                   ________    ________    _______    __________

    (Intercept)      72.001      5.1971     13.854    1.0078e-24
    Weight         0.056651    0.025884     2.1887       0.03102
    Age            0.058378    0.095319    0.61245       0.54167


Number of observations: 100, Error degrees of freedom: 97
Root Mean Squared Error: 6.81
R-squared: 0.0533,  Adjusted R-Squared: 0.0337
F-statistic vs. constant model: 2.73, p-value = 0.0704

Answer 1

[David Hill]

Save in a cell array. I might not be understanding you completely.

for k=1:10
    Model=%your code
    c{k}=Model;
end

Answer 2

[Nathan]

If you're looking for what I was looking for Model_1.Coefficients contained a table of coefficients

load patients Weight Age Diastolic
x = [Weight, Age];
Model_1 = fitlm (x, Diastolic, 'VarNames', {'Weight', 'Age', 'DBP'})

Model_1 = 
Linear regression model:
    DBP ~ 1 + Weight + Age

Estimated Coefficients:
                   Estimate       SE        tStat       pValue  
                   ________    ________    _______    __________

    (Intercept)      72.001      5.1971     13.854    1.0078e-24
    Weight         0.056651    0.025884     2.1887       0.03102
    Age            0.058378    0.095319    0.61245       0.54167


Number of observations: 100, Error degrees of freedom: 97
Root Mean Squared Error: 6.81
R-squared: 0.0533,  Adjusted R-Squared: 0.0337
F-statistic vs. constant model: 2.73, p-value = 0.0704

Model_1.Coefficients

ans = 3×4 table
                   Estimate       SE        tStat       pValue  
                   ________    ________    _______    __________

    (Intercept)      72.001      5.1971     13.854    1.0078e-24
    Weight         0.056651    0.025884     2.1887       0.03102
    Age            0.058378    0.095319    0.61245       0.54167