For Linear Probability Model:

Open Minitab and load your data into the worksheet.

Choose 'Stat $>$ Regression $>$ Regression' to open the regression dialog box.

Select the dependent variable $($the outcome of complications, which is likely binary coded as $0$

or $1).$

Select the independent variables $($weight$,$ age, and diabetes status$).$

Click $\text{'}$OK$\text{'}$ to run the regression and get the output.

For Logistic Regression:

Open Minitab and load your data into the worksheet.

Choose 'Stat $>$ Regression $>$ Binary Logistic Regression'.

Select the dependent variable $($again$,$ the binary outcome variable$).$

Select the independent variables $($weight$,$ age, diabetes status$).$

You can choose additional options such as odds ratios or confidence intervals as needed.

Click $\text{'}OK\text{'}$ to run the analysis.

For Prediction:

After running the preferred model $($based on model diagnostics, goodness$-$of$-$fit, etc.$),$ you can

use the fitted model to predict outcomes.

In the case of logistic regression, use 'Stat $>$ Regression $>$ Binary Logistic Regression

$>$ Predict' to predict the probability for a given set of inputs.

Enter the values for the $60-$year$-$old diabetic patient with a weight of $180$ pounds to get the

predicted probability of complications.

STEM GPA SAT White Female Asian

$03\mathrm{.}701420001$

$04\mathrm{.}401240011$

$12\mathrm{.}901450000$

$12\mathrm{.}901520010$

$14\mathrm{.}001530010$

$13\mathrm{.}201500110$

$14\mathrm{.}201490011$

$03\mathrm{.}501140010$

$02\mathrm{.}901390110$

$04\mathrm{.}301100100$

$04\mathrm{.}001380110$

$04\mathrm{.}301200001$

$13\mathrm{.}801600010$

$03\mathrm{.}201070001$

$04\mathrm{.}101070110$

$13\mathrm{.}401450110$

$03\mathrm{.}201320010$

$04\mathrm{.}101350000$

$04\mathrm{.}101140000$

$13\mathrm{.}701370011$

$03\mathrm{.}601050100$

$04\mathrm{.}201290000$

$14\mathrm{.}301590010$

$04\mathrm{.}301370100$

$13\mathrm{.}801430000$

$13\mathrm{.}201480000$

$04\mathrm{.}001210001$

$04\mathrm{.}301310010$

$02\mathrm{.}801240000$

$14\mathrm{.}401390000$

$13\mathrm{.}301560100$

$02\mathrm{.}801350010$

$13\mathrm{.}001540010$

$02\mathrm{.}501110000$

$12\mathrm{.}701350010$

$14\mathrm{.}001340000$

$12\mathrm{.}501450100$

$12\mathrm{.}601460000$

$14\mathrm{.}501350001$

$03\mathrm{.}701140100$

$03\mathrm{.}701290110$

$02\mathrm{.}701030100$

$12\mathrm{.}901570110$

$04\mathrm{.}301180000$

$04\mathrm{.}201100000$

$03\mathrm{.}601140000$

$13\mathrm{.}701490001$

$13\mathrm{.}101360100$

$03\mathrm{.}701060110$

$03\mathrm{.}101100001$

$04\mathrm{.}101100110$

$02\mathrm{.}601370110$

$04\mathrm{.}101240011$

$03\mathrm{.}501060100$

$03\mathrm{.}801180110$

$13\mathrm{.}801460110$

$13\mathrm{.}301350011$

$13\mathrm{.}801400000$

$12\mathrm{.}701530001$

$04\mathrm{.}201110100$

$03\mathrm{.}101170001$

$04\mathrm{.}101070110$

$03\mathrm{.}201170000$

$03\mathrm{.}301340000$

$04\mathrm{.}401030110$

$02\mathrm{.}801320000$

$12\mathrm{.}901340100$

$12\mathrm{.}701520010$

$13\mathrm{.}901440011$

$03\mathrm{.}901180110$

$03\mathrm{.}701440110$

$03\mathrm{.}401190110$

$13\mathrm{.}201380100$

$02\mathrm{.}801430010$

$13\mathrm{.}801270100$

$03\mathrm{.}601210100$

$14\mathrm{.}401470010$

$12\mathrm{.}901560110$

$14\mathrm{.}501500000$

$13\mathrm{.}801380001$

$02\mathrm{.}601020010$

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