Can I Run a Logistic Regression in Excel? Here's How (and When to Skip It)

You’ve got a dataset-maybe customer churn, delivery failure rates, or whether a shipment got delayed-and you want to know what’s driving it. You think: logistic regression. But you don’t have Python, R, or a fancy stats tool. You’re stuck with Excel. Can you actually run a logistic regression in Excel? Yes. But should you? Let’s cut through the noise.

What logistic regression actually does

Logistic regression predicts the probability of a yes-or-no outcome. Will this package arrive on time? Will this customer cancel? Will this driver get a violation? It doesn’t give you a number like "7.3 days"-it gives you a percentage, like "82% chance of on-time delivery". That’s useful when you’re making decisions based on risk, not exact values.

Unlike linear regression, which assumes a straight-line relationship between variables, logistic regression handles outcomes that are binary: yes/no, 1/0, success/failure. It uses a special curve called the sigmoid function to squeeze results between 0 and 1. That’s why it’s perfect for logistics problems where you’re dealing with events that either happen or don’t.

How to run logistic regression in Excel (step by step)

Excel doesn’t have a one-click button for logistic regression. But you can build it using Solver and some basic formulas. Here’s how:

1. Organize your data: One column for the outcome (0 or 1), and one or more columns for predictors-like distance, weather, time of day, or carrier score.
2. Set up initial coefficients: In a row, assign a starting value of 0.01 to each predictor (e.g., B2 for distance, C2 for weather, etc.). These are your regression weights.
3. Create a logit column: In a new column, use the formula =B$2*B3 + C$2*C3 + D$2*D3 (adjusting cell references for your data). This calculates the linear combination of predictors.
4. Create predicted probabilities: In the next column, use =1/(1+EXP(-E3)) (where E3 is the logit). This turns the logit into a probability between 0 and 1.
5. Calculate log-likelihood: In another column, use =IF(F3=1, LN(G3), LN(1-G3)) (where F3 is your actual outcome and G3 is the predicted probability).
6. Sum the log-likelihoods: Add up all the values from step 5. This is your total log-likelihood.
7. Use Solver: Go to Data → Solver. Set the objective to maximize the sum of log-likelihoods. Set changing cells to your coefficient row (B2, C2, D2…). Click Solve.

After Solver finishes, your coefficients are optimized. You can now plug in new data to predict outcomes. For example, if distance = 120 miles and weather score = 3, multiply each by its coefficient and run the sigmoid formula to get your probability.

Why this method is messy (and when it fails)

Excel’s approach works for small datasets-say, under 500 rows. But it’s fragile. Solver doesn’t always converge. If your data has perfect separation (e.g., every shipment over 150 miles always fails), Excel will throw an error or give nonsense coefficients. It also can’t handle multicollinearity well. If two predictors are highly correlated-like "distance" and "fuel cost"-the model gets unstable.

And forget about p-values, confidence intervals, or model diagnostics. Excel doesn’t calculate them automatically. You’d need to code them manually using chi-square tests and standard error formulas, which is error-prone and time-consuming.

Real-world logistics data is rarely clean. You’ll have missing values, outliers, categorical variables like "carrier name" or "region". Excel can’t handle those without a ton of dummy coding and manual cleanup. If you’re working with 10,000+ records or 15+ predictors, you’re already in over your head.

When Excel is actually okay

There are times when Excel makes sense:

• You’re testing a hypothesis on a small pilot dataset (e.g., 200 delivery records from one warehouse).
• You’re a manager who needs to explain probabilities to a team without coding skills.
• You’re validating results from a real model-using Excel as a quick sanity check.
• You don’t have access to other tools and need a temporary solution.

For example, a warehouse supervisor might use Excel to see if weekend shipments are more likely to be delayed. With 300 records and two variables (day of week and weather), Excel gives a usable answer. It’s not perfect-but it’s faster than waiting for IT to set up a Python environment.

What to use instead (and why)

If you’re serious about logistics analytics, Excel isn’t the endgame. Here’s what you should move toward:

• Python (with scikit-learn): Two lines of code: from sklearn.linear_model import LogisticRegression and model.fit(X, y). It handles missing data, categorical variables, regularization, and gives you full diagnostics.
• R: The language built for stats. The glm() function runs logistic regression with p-values, AIC, and residual plots built in.
• Google Sheets + Apps Script: If you’re cloud-only, you can script logistic regression in Sheets using JavaScript. Not ideal, but better than Excel’s Solver.
• Power BI with R/Python integration: Connect your Excel data to Power BI and run the model inside the dashboard. You get visuals and interactivity without leaving your reporting tool.

Logistics companies like DHL and FedEx use machine learning pipelines that update daily. They don’t rely on Excel. But they started somewhere. Many analysts begin with Excel, then graduate to Python when the limits hit.

Practical checklist: Should you use Excel for logistic regression?

Ask yourself these questions before you start:

• Do I have fewer than 500 rows of clean data? → Yes? Proceed.
• Are my predictors all numeric and not highly correlated? → Yes? Okay.
• Do I need p-values, confidence intervals, or model diagnostics? → No? Then Excel might work.
• Will this analysis be used for decision-making beyond one team? → Probably not. Use something better.
• Am I willing to debug Solver errors for hours? → If not, skip it.

If you answered "no" to any of the first three, don’t waste your time. Use a free tool like RStudio or Jupyter Notebook. Both are free, and there are tutorials that walk you through logistic regression in under 15 minutes.

What happens if you ignore the limits of Excel?

One logistics manager I know ran a logistic regression in Excel to predict delivery delays. He used 12 variables, including "driver ID" and "truck license plate". Solver "converged" and gave him a model with 98% accuracy. He presented it to his boss as proof the system was working.

Turns out, the model was memorizing the data. It had perfect separation: every delay was tied to a single driver who was fired two weeks later. The model didn’t predict anything-it just repeated history. And Excel didn’t warn him.

Without proper validation, logistic regression in Excel can give you false confidence. That’s worse than not doing it at all.

Final advice

Can you run logistic regression in Excel? Technically, yes. But it’s like using a hammer to install a screw. You can do it. But you’ll bend the screw, hurt your hand, and still not get it right.

Use Excel for quick exploration. Use it to clean data, spot trends, or build simple dashboards. But when you need real predictive power, move to a tool built for statistics. The time you save in accuracy and reliability will pay off faster than you think.

Start with a free Python tutorial. Learn how to load your Excel file into pandas, run LogisticRegression(), and plot the results. You’ll be doing real logistics analytics in a weekend. And you won’t have to pray to Solver.

If you're stuck with Excel today, use it to learn. But don’t let it be your long-term solution. The next step up isn’t hard-it’s just a few tutorials away.